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关于后门攻击&防御的博客与论文。

ECCV2022对抗攻击&防御论文汇总 | Li's Blog (tuoli9.github.io)

ICLR2022对抗攻击&防御论文汇总 | Li's Blog (tuoli9.github.io)

CVPR2022对抗攻击&防御论文汇总 | Li's Blog (tuoli9.github.io)

 ACM MM2022对抗攻击&防御论文汇总 | Li's Blog (tuoli9.github.io)

AAAI2022对抗攻击&防御论文汇总 | Li's Blog (tuoli9.github.io) 

NIPS2022对抗攻击&防御论文汇总 | Li's Blog (tuoli9.github.io) 

THUYimingLi/backdoor-learning-resources: A list of backdoor learning resources (github.com)

目录

Survey

Toolbox

Dissertation and Thesis

Image and Video Classification

Poisoning-based Attack

Non-poisoning-based Attack

Backdoor Defense

Attack and Defense Towards Other Paradigms and Tasks

Federated Learning

Transfer Learning

Reinforcement Learning

Semi-Supervised and Self-Supervised Learning

Quantization

Natural Language Processing

Graph Neural Networks

Point Cloud

Acoustics Signal Processing

Medical Science

Cybersecurity

Others

Evaluation and Discussion

Backdoor Attack for Positive Purposes

Competition

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Survey

• Backdoor Learning: A Survey. [pdf]

  • Yiming Li, Yong Jiang, Zhifeng Li, and Shu-Tao Xia. IEEE Transactions on Neural Networks and Learning Systems, 2022.

• Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review. [pdf]

  • Yansong Gao, Bao Gia Doan, Zhi Zhang, Siqi Ma, Anmin Fu, Surya Nepal, and Hyoungshick Kim. arXiv, 2020.

• Data Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses. [pdf]

  • Micah Goldblum, Dimitris Tsipras, Chulin Xie, Xinyun Chen, Avi Schwarzschild, Dawn Song, Aleksander Madry, Bo Li, and Tom Goldstein. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.

• A Comprehensive Survey on Poisoning Attacks and Countermeasures in Machine Learning. [link]

  • Zhiyi Tian, Lei Cui, Jie Liang, and Shui Yu. ACM Computing Surveys, 2022.

• Backdoor Attacks and Defenses in Federated Learning: State-of-the-art, Taxonomy, and Future Directions. [link]

  • Xueluan Gong, Yanjiao Chen, Qian Wang, and Weihan Kong. IEEE Wireless Communications, 2022.

• Backdoor Attacks on Image Classification Models in Deep Neural Networks. [link]

  • Quanxin Zhang, Wencong Ma, Yajie Wang, Yaoyuan Zhang, Zhiwei Shi, and Yuanzhang Li. Chinese Journal of Electronics, 2022.

• Defense against Neural Trojan Attacks: A Survey. [link]

  • Sara Kaviani and Insoo Sohn. Neurocomputing, 2021.

• A Survey on Neural Trojans. [pdf]

  • Yuntao Liu, Ankit Mondal, Abhishek Chakraborty, Michael Zuzak, Nina Jacobsen, Daniel Xing, and Ankur Srivastava. ISQED, 2020.

• A Survey of Neural Trojan Attacks and Defenses in Deep Learning. [pdf]

  • Jie Wang, Ghulam Mubashar Hassan, and Naveed Akhtar. arXiv, 2022.

• Threats to Pre-trained Language Models: Survey and Taxonomy. [pdf]

  • Shangwei Guo, Chunlong Xie, Jiwei Li, Lingjuan Lyu, and Tianwei Zhang. arXiv, 2022.

• An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences. [pdf]

  • Wei Guo, Benedetta Tondi, and Mauro Barni. arXiv, 2021.

• Deep Learning Backdoors. [pdf]

  • Shaofeng Li, Shiqing Ma, Minhui Xue, and Benjamin Zi Hao Zhao. arXiv, 2020.

Toolbox

• BackdoorBox
• TrojanZoo
• OpenBackdoor
• Backdoor Toolbox
• BackdoorBench
• backdoors101
• ART

Dissertation and Thesis

• Defense of Backdoor Attacks against Deep Neural Network Classifiers. [pdf]

  • Zhen Xiang. Ph.D. Dissertation at The Pennsylvania State University, 2022.

• Towards Adversarial and Backdoor Robustness of Deep Learning. [link]

  • Yifan Guo. Ph.D. Dissertation at Case Western Reserve University, 2022.

• Toward Robust and Communication Efficient Distributed Machine Learning. [pdf]

  • Hongyi Wang. Ph.D. Dissertation at University of Wisconsin–Madison, 2021.

• Towards Robust Image Classification with Deep Learning and Real-Time DNN Inference on Mobile. [pdf]

  • Pu Zhao. Ph.D. Dissertation at Northeastern University, 2021.

• Countermeasures Against Backdoor, Data Poisoning, and Adversarial Attacks. [pdf]

  • Henry Daniel. Ph.D. Dissertation at University of Texas at San Antonio, 2021.

• Understanding and Mitigating the Impact of Backdooring Attacks on Deep Neural Networks. [pdf]

  • Kang Liu. Ph.D. Dissertation at New York University, 2021.

• Un-fair trojan: Targeted Backdoor Attacks against Model Fairness. [pdf]

  • Nicholas Furth. Master Thesis at New Jersey Institute of Technology, 2022.

• Check Your Other Door: Creating Backdoor Attacks in the Frequency Domain. [pdf]

  • Hasan Abed Al Kader Hammoud. Master Thesis at King Abdullah University of Science and Technology, 2022.

• Backdoor Attacks in Neural Networks. [link]

  • Stefanos Koffas. Master Thesis at Delft University of Technology, 2021.

• Backdoor Defenses. [pdf]

  • Andrea Milakovic. Master Thesis at Technischen Universität Wien, 2021.

• Geometric Properties of Backdoored Neural Networks. [pdf]

  • Dominic Carrano. Master Thesis at University of California at Berkeley, 2021.

• Detecting Backdoored Neural Networks with Structured Adversarial Attacks. [pdf]

  • Charles Yang. Master Thesis at University of California at Berkeley, 2021.

• Backdoor Attacks Against Deep Learning Systems in the Physical World. [pdf]

  • Emily Willson. Master Thesis at University of Chicago, 2020.

Image and Video Classification

Poisoning-based Attack

2022

• Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection. [pdf] [code]

  • Yiming Li, Yang Bai, Yong Jiang, Yong Yang, Shu-Tao Xia, and Bo Li. NeurIPS, 2022.

• DEFEAT: Deep Hidden Feature Backdoor Attacks by Imperceptible Perturbation and Latent Representation Constraints. [pdf]

  • Zhendong Zhao, Xiaojun Chen, Yuexin Xuan, Ye Dong, Dakui Wang, and Kaitai Liang. CVPR, 2022.

• An Invisible Black-box Backdoor Attack through Frequency Domain. [pdf] [code]

  • Tong Wang, Yuan Yao, Feng Xu, Shengwei An, Hanghang Tong, and Ting Wang. ECCV, 2022.

• BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial Learning. [pdf] [code]

  • Zhenting Wang, Juan Zhai, and Shiqing Ma. CVPR, 2022.

• Dynamic Backdoor Attacks Against Machine Learning Models. [pdf]

  • Ahmed Salem, Rui Wen, Michael Backes, Shiqing Ma, and Yang Zhang. EuroS&P, 2022.

• Imperceptible Backdoor Attack: From Input Space to Feature Representation. [pdf] [code]

  • Nan Zhong, Zhenxing Qian, and Xinpeng Zhang. IJCAI, 2022.

• Stealthy Backdoor Attack with Adversarial Training. [link]

  • Le Feng, Sheng Li, Zhenxing Qian, and Xinpeng Zhang. ICASSP, 2022.

• Invisible and Efficient Backdoor Attacks for Compressed Deep Neural Networks. [link]

  • Huy Phan, Yi Xie, Jian Liu, Yingying Chen, and Bo Yuan. ICASSP, 2022.

• Dynamic Backdoors with Global Average Pooling. [pdf]

  • Stefanos Koffas, Stjepan Picek, and Mauro Conti. AICAS, 2022.

• Poison Ink: Robust and Invisible Backdoor Attack. [pdf]

  • Jie Zhang, Dongdong Chen, Qidong Huang, Jing Liao, Weiming Zhang, Huamin Feng, Gang Hua, and Nenghai Yu. IEEE Transactions on Image Processing, 2022.

• Enhancing Backdoor Attacks with Multi-Level MMD Regularization. [link]

  • Pengfei Xia, Hongjing Niu, Ziqiang Li, and Bin Li. IEEE Transactions on Dependable and Secure Computing, 2022.

• PTB: Robust Physical Backdoor Attacks against Deep Neural Networks in Real World. [link]

  • Mingfu Xue, Can He, Yinghao Wu, Shichang Sun, Yushu Zhang, Jian Wang, and Weiqiang Liu. Computers & Security, 2022.

• IBAttack: Being Cautious about Data Labels. [link]

  • Akshay Agarwal, Richa Singh, Mayank Vatsa, and Nalini Ratha. IEEE Transactions on Artificial Intelligence, 2022.

• BlindNet Backdoor: Attack on Deep Neural Network using Blind Watermark. [link]

  • Hyun Kwon and Yongchul Kim. Multimedia Tools and Applications, 2022.

• Natural Backdoor Attacks on Deep Neural Networks via Raindrops. [link]

  • Feng Zhao, Li Zhou, Qi Zhong, Rushi Lan, and Leo Yu Zhang. Security and Communication Networks, 2022.

• Dispersed Pixel Perturbation-based Imperceptible Backdoor Trigger for Image Classifier Models. [pdf]

  • Yulong Wang, Minghui Zhao, Shenghong Li, Xin Yuan, and Wei Ni. arXiv, 2022.

• FRIB: Low-poisoning Rate Invisible Backdoor Attack based on Feature Repair. [pdf]

  • Hui Xia, Xiugui Yang, Xiangyun Qian, and Rui Zhang. arXiv, 2022.

• Augmentation Backdoors. [pdf] [code]

  • Joseph Rance, Yiren Zhao, Ilia Shumailov, and Robert Mullins. arXiv, 2022.

• Just Rotate it: Deploying Backdoor Attacks via Rotation Transformation. [pdf]

  • Tong Wu, Tianhao Wang, Vikash Sehwag, Saeed Mahloujifar, and Prateek Mittal. arXiv, 2022.

• Natural Backdoor Datasets. [pdf]

  • Emily Wenger, Roma Bhattacharjee, Arjun Nitin Bhagoji, Josephine Passananti, Emilio Andere, Haitao Zheng, and Ben Y. Zhao. arXiv, 2022.

• Backdoor Attacks on Vision Transformers. [pdf] [code]

  • Akshayvarun Subramanya, Aniruddha Saha, Soroush Abbasi Koohpayegani, Ajinkya Tejankar, and Hamed Pirsiavash. arXiv, 2022.

• Enhancing Clean Label Backdoor Attack with Two-phase Specific Triggers. [pdf]

  • Nan Luo, Yuanzhang Li, Yajie Wang, Shangbo Wu, Yu-an Tan, and Quanxin Zhang. arXiv, 2022.

• Circumventing Backdoor Defenses That Are Based on Latent Separability. [pdf] [code]

  • Xiangyu Qi, Tinghao Xie, Saeed Mahloujifar, and Prateek Mittal. arXiv, 2022.

• Narcissus: A Practical Clean-Label Backdoor Attack with Limited Information. [pdf]

  • Yi Zeng, Minzhou Pan, Hoang Anh Just, Lingjuan Lyu, Meikang Qiu, and Ruoxi Jia. arXiv, 2022.

• CASSOCK: Viable Backdoor Attacks against DNN in The Wall of Source-Specific Backdoor Defences. [pdf]

  • Shang Wang, Yansong Gao, Anmin Fu, Zhi Zhang, Yuqing Zhang, and Willy Susilo. arXiv, 2022.

• Trojan Horse Training for Breaking Defenses against Backdoor Attacks in Deep Learning. [pdf]

  • Arezoo Rajabi, Bhaskar Ramasubramanian, and Radha Poovendran. arXiv, 2022.

• Label-Smoothed Backdoor Attack. [pdf]

  • Minlong Peng, Zidi Xiong, Mingming Sun, and Ping Li. arXiv, 2022.

• Imperceptible and Multi-channel Backdoor Attack against Deep Neural Networks. [pdf]

  • Mingfu Xue, Shifeng Ni, Yinghao Wu, Yushu Zhang, Jian Wang, and Weiqiang Liu. arXiv, 2022.

• Compression-Resistant Backdoor Attack against Deep Neural Networks. [pdf]

  • Mingfu Xue, Xin Wang, Shichang Sun, Yushu Zhang, Jian Wang, and Weiqiang Liu. arXiv, 2022.

2021

• Invisible Backdoor Attack with Sample-Specific Triggers. [pdf] [code]

  • Yuezun Li, Yiming Li, Baoyuan Wu, Longkang Li, Ran He, and Siwei Lyu. ICCV, 2021.

• Manipulating SGD with Data Ordering Attacks. [pdf]

  • Ilia Shumailov, Zakhar Shumaylov, Dmitry Kazhdan, Yiren Zhao, Nicolas Papernot, Murat A. Erdogdu, and Ross Anderson. NeurIPS, 2021.

• Backdoor Attack with Imperceptible Input and Latent Modification. [pdf]

  • Khoa Doan, Yingjie Lao, and Ping Li. NeurIPS, 2021.

• LIRA: Learnable, Imperceptible and Robust Backdoor Attacks. [pdf]

  • Khoa Doan, Yingjie Lao, Weijie Zhao, and Ping Li. ICCV, 2021.

• Blind Backdoors in Deep Learning Models. [pdf] [code]

  • Eugene Bagdasaryan, and Vitaly Shmatikov. USENIX Security, 2021.

• Backdoor Attacks Against Deep Learning Systems in the Physical World. [pdf] [Master Thesis]

  • Emily Wenger, Josephine Passanati, Yuanshun Yao, Haitao Zheng, and Ben Y. Zhao. CVPR, 2021.

• Deep Feature Space Trojan Attack of Neural Networks by Controlled Detoxification. [pdf] [code]

  • Siyuan Cheng, Yingqi Liu, Shiqing Ma, and Xiangyu Zhang. AAAI, 2021.

• WaNet - Imperceptible Warping-based Backdoor Attack. [pdf] [code]

  • Tuan Anh Nguyen, and Anh Tuan Tran. ICLR, 2021.

• AdvDoor: Adversarial Backdoor Attack of Deep Learning System. [pdf] [code]

  • Quan Zhang, Yifeng Ding, Yongqiang Tian, Jianmin Guo, Min Yuan, and Yu Jiang. ISSTA, 2021.

• Invisible Poison: A Blackbox Clean Label Backdoor Attack to Deep Neural Networks. [pdf]

  • Rui Ning, Jiang Li, ChunSheng Xin, and Hongyi Wu. INFOCOM, 2021.

• Backdoor Attack in the Physical World. [pdf] [extension]

  • Yiming Li, Tongqing Zhai, Yong Jiang, Zhifeng Li, and Shu-Tao Xia. ICLR Workshop, 2021.

• Defense-Resistant Backdoor Attacks against Deep Neural Networks in Outsourced Cloud Environment. [Link]

  • Xueluan Gong, Yanjiao Chen, Qian Wang, Huayang Huang, Lingshuo Meng, Chao Shen, and Qian Zhang. IEEE Journal on Selected Areas in Communications, 2021.

• A Master Key Backdoor for Universal Impersonation Attack against DNN-based Face Verification. [link]

  • WeiGuo, Benedetta Tondi, and Mauro Barni. Pattern Recognition Letters, 2021.

• Backdoors Hidden in Facial Features: A Novel Invisible Backdoor Attack against Face Recognition Systems. [link]

  • Mingfu Xue, Can He, Jian Wang, and Weiqiang Liu. Peer-to-Peer Networking and Applications, 2021.

• Use Procedural Noise to Achieve Backdoor Attack. [link] [code]

  • Xuan Chen, Yuena Ma, and Shiwei Lu. IEEE Access, 2021.

• A Multitarget Backdooring Attack on Deep Neural Networks with Random Location Trigger. [link]

  • Xiao Yu, Cong Liu, Mingwen Zheng, Yajie Wang, Xinrui Liu, Shuxiao Song, Yuexuan Ma, and Jun Zheng. International Journal of Intelligent Systems, 2021.

• Simtrojan: Stealthy Backdoor Attack. [link]

  • Yankun Ren, Longfei Li, and Jun Zhou. ICIP, 2021.

• DBIA: Data-free Backdoor Injection Attack against Transformer Networks. [pdf] [code]

  • Peizhuo Lv, Hualong Ma, Jiachen Zhou, Ruigang Liang, Kai Chen, Shengzhi Zhang, and Yunfei Yang. arXiv, 2021.

• A Statistical Difference Reduction Method for Escaping Backdoor Detection. [pdf]

  • Pengfei Xia, Hongjing Niu, Ziqiang Li, and Bin Li. arXiv, 2021.

• Backdoor Attack through Frequency Domain. [pdf]

  • Tong Wang, Yuan Yao, Feng Xu, Shengwei An, and Ting Wang. arXiv, 2021.

• Check Your Other Door! Establishing Backdoor Attacks in the Frequency Domain. [pdf]

  • Hasan Abed Al Kader Hammoud and Bernard Ghanem. arXiv, 2021.

• Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch. [pdf] [code]

  • Hossein Souri, Micah Goldblum, Liam Fowl, Rama Chellappa, and Tom Goldstein. arXiv, 2021.

• RABA: A Robust Avatar Backdoor Attack on Deep Neural Network. [pdf]

  • Ying He, Zhili Shen, Chang Xia, Jingyu Hua, Wei Tong, and Sheng Zhong. arXiv, 2021.

• Robust Backdoor Attacks against Deep Neural Networks in Real Physical World. [pdf]

  • Mingfu Xue, Can He, Shichang Sun, Jian Wang, and Weiqiang Liu. arXiv, 2021.

2020

• Composite Backdoor Attack for Deep Neural Network by Mixing Existing Benign Features. [pdf]

  • Junyu Lin, Lei Xu, Yingqi Liu, Xiangyu Zhang. CCS, 2020.

• Input-Aware Dynamic Backdoor Attack. [pdf] [code]

  • Anh Nguyen, and Anh Tran. NeurIPS, 2020.

• Bypassing Backdoor Detection Algorithms in Deep Learning. [pdf]

  • Te Juin Lester Tan, and Reza Shokri. EuroS&P, 2020.

• Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation. [pdf]

  • Cong Liao, Haoti Zhong, Anna Squicciarini, Sencun Zhu, and David Miller. ACM CODASPY, 2020.

• Clean-Label Backdoor Attacks on Video Recognition Models. [pdf] [code]

  • Shihao Zhao, Xingjun Ma, Xiang Zheng, James Bailey, Jingjing Chen, and Yu-Gang Jiang. CVPR, 2020.

• Escaping Backdoor Attack Detection of Deep Learning. [link]

  • Yayuan Xiong, Fengyuan Xu, Sheng Zhong, and Qun Li. IFIP SEC, 2020.

• Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks. [pdf] [code]

  • Yunfei Liu, Xingjun Ma, James Bailey, and Feng Lu. ECCV, 2020.

• Live Trojan Attacks on Deep Neural Networks. [pdf] [code]

  • Robby Costales, Chengzhi Mao, Raphael Norwitz, Bryan Kim, and Junfeng Yang. CVPR Workshop, 2020.

• Backdooring and Poisoning Neural Networks with Image-Scaling Attacks. [pdf]

  • Erwin Quiring, and Konrad Rieck. IEEE S&P Workshop, 2020.

• One-to-N & N-to-One: Two Advanced Backdoor Attacks against Deep Learning Models. [pdf]

  • Mingfu Xue, Can He, Jian Wang, and Weiqiang Liu. IEEE Transactions on Dependable and Secure Computing, 2020.

• Invisible Backdoor Attacks on Deep Neural Networks via Steganography and Regularization. [pdf] [arXiv Version (2019)]

  • Shaofeng Li, Minhui Xue, Benjamin Zi Hao Zhao, Haojin Zhu, and Xinpeng Zhang. IEEE Transactions on Dependable and Secure Computing, 2020.

• HaS-Nets: A Heal and Select Mechanism to Defend DNNs Against Backdoor Attacks for Data Collection Scenarios. [pdf]

  • Hassan Ali, Surya Nepal, Salil S. Kanhere, and Sanjay Jha. arXiv, 2020.

• FaceHack: Triggering Backdoored Facial Recognition Systems Using Facial Characteristics. [pdf]

  • Esha Sarkar, Hadjer Benkraouda, and Michail Maniatakos. arXiv, 2020.

• Light Can Hack Your Face! Black-box Backdoor Attack on Face Recognition Systems. [pdf]

  • Haoliang Li, Yufei Wang, Xiaofei Xie, Yang Liu, Shiqi Wang, Renjie Wan, Lap-Pui Chau, and Alex C. Kot. arXiv, 2020.

2019

• A New Backdoor Attack in CNNS by Training Set Corruption Without Label Poisoning. [pdf]

  • M.Barni, K.Kallas, and B.Tondi. ICIP, 2019.

• Label-Consistent Backdoor Attacks. [pdf] [code]

  • Alexander Turner, Dimitris Tsipras, and Aleksander Madry. arXiv, 2019.

2018

• Trojaning Attack on Neural Networks. [pdf] [code]
  • Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, and Juan Zhai. NDSS, 2018.

2017

• BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain. [pdf] [journal]

  • Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg. arXiv, 2017 (IEEE Access, 2019).

• Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning. [pdf] [code]

  • Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song. arXiv, 2017.

Non-poisoning-based Attack

Weights-oriented Attack

• Handcrafted Backdoors in Deep Neural Networks. [pdf]

  • Sanghyun Hong, Nicholas Carlini, and Alexey Kurakin. NeurIPS, 2022.

• Hardly Perceptible Trojan Attack against Neural Networks with Bit Flips. [pdf] [code]

  • Jiawang Bai, Kuofeng Gao, Dihong Gong, Shu-Tao Xia, Zhifeng Li, and Wei Liu. ECCV, 2022.

• ProFlip: Targeted Trojan Attack with Progressive Bit Flips. [pdf]

  • Huili Chen, Cheng Fu, Jishen Zhao, and Farinaz Koushanfar. ICCV, 2021.

• TBT: Targeted Neural Network Attack with Bit Trojan. [pdf] [code]

  • Adnan Siraj Rakin, Zhezhi He, and Deliang Fan. CVPR, 2020.

• How to Inject Backdoors with Better Consistency: Logit Anchoring on Clean Data. [pdf]

  • Zhiyuan Zhang, Lingjuan Lyu, Weiqiang Wang, Lichao Sun, and Xu Sun. ICLR, 2022.

• Can Adversarial Weight Perturbations Inject Neural Backdoors? [pdf]

  • Siddhant Garg, Adarsh Kumar, Vibhor Goel, and Yingyu Liang. CIKM, 2020.

• TrojViT: Trojan Insertion in Vision Transformers. [pdf]

  • Mengxin Zheng, Qian Lou, and Lei Jiang. arXiv, 2022.

• Versatile Weight Attack via Flipping Limited Bits. [pdf]

  • Jiawang Bai, Baoyuan Wu, Zhifeng Li, and Shu-Tao Xia. arXiv, 2022.

• Toward Realistic Backdoor Injection Attacks on DNNs using Rowhammer. [pdf]

  • M. Caner Tol, Saad Islam, Berk Sunar, and Ziming Zhang. 2022.

• TrojanNet: Embedding Hidden Trojan Horse Models in Neural Network. [pdf]

  • Chuan Guo, Ruihan Wu, and Kilian Q. Weinberger. arXiv, 2020.

• Backdooring Convolutional Neural Networks via Targeted Weight Perturbations. [pdf]

  • Jacob Dumford, and Walter Scheirer. arXiv, 2018.

Structure-modified Attack

• LoneNeuron: a Highly-Effective Feature-Domain Neural Trojan Using Invisible and Polymorphic Watermarks. [pdf]

  • Zeyan Liu, Fengjun Li, Zhu Li, and Bo Luo. CCS, 2022.

• Towards Practical Deployment-Stage Backdoor Attack on Deep Neural Networks. [pdf] [code]

  • Xiangyu Qi, Tinghao Xie, Ruizhe Pan, Jifeng Zhu, Yong Yang, and Kai Bu. CVPR, 2022.

• Hiding Needles in a Haystack: Towards Constructing Neural Networks that Evade Verification. [link] [code]

  • Árpád Berta, Gábor Danner, István Hegedűs and Márk Jelasity. ACM IH&MMSec, 2022.

• Stealthy and Flexible Trojan in Deep Learning Framework. [link]

  • Yajie Wang, Kongyang Chen, Yu-An Tan, Shuxin Huang, Wencong Ma, and Yuanzhang Li. IEEE Transactions on Dependable and Secure Computing, 2022.

• FooBaR: Fault Fooling Backdoor Attack on Neural Network Training. [link] [code]

  • Jakub Breier, Xiaolu Hou, Martín Ochoa and Jesus Solano. IEEE Transactions on Dependable and Secure Computing, 2022.

• DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection. [pdf]

  • Yuanchun Li, Jiayi Hua, Haoyu Wang, Chunyang Chen, and Yunxin Liu. ICSE, 2021.

• An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks. [pdf] [code]

  • Ruixiang Tang, Mengnan Du, Ninghao Liu, Fan Yang, and Xia Hu. KDD, 2020.

• BadRes: Reveal the Backdoors through Residual Connection. [pdf]

  • Mingrui He, Tianyu Chen, Haoyi Zhou, Shanghang Zhang, and Jianxin Li. arXiv, 2022.

• Architectural Backdoors in Neural Networks. [pdf]

  • Mikel Bober-Irizar, Ilia Shumailov, Yiren Zhao, Robert Mullins, and Nicolas Papernot. arXiv, 2022.

• Planting Undetectable Backdoors in Machine Learning Models. [pdf]

  • Shafi Goldwasser, Michael P. Kim, Vinod Vaikuntanathan, and Or Zamir. arXiv, 2022.

Other Attacks

• ImpNet: Imperceptible and blackbox-undetectable backdoors in compiled neural networks. [pdf] [website] [code]

  • Tim Clifford, Ilia Shumailov, Yiren Zhao, Ross Anderson, and Robert Mullins. arXiv, 2022.

• Don't Trigger Me! A Triggerless Backdoor Attack Against Deep Neural Networks. [pdf]

  • Ahmed Salem, Michael Backes, and Yang Zhang. arXiv, 2020.

Backdoor Defense

Preprocessing-based Empirical Defense

• Backdoor Attack in the Physical World. [pdf] [extension]

  • Yiming Li, Tongqing Zhai, Yong Jiang, Zhifeng Li, and Shu-Tao Xia. ICLR Workshop, 2021.

• DeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation. [pdf] [code]

  • Han Qiu, Yi Zeng, Shangwei Guo, Tianwei Zhang, Meikang Qiu, and Bhavani Thuraisingham. AsiaCCS, 2021.

• Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems. [pdf] [code]

  • Bao Gia Doan, Ehsan Abbasnejad, and Damith C. Ranasinghe. ACSAC, 2020.

• Neural Trojans. [pdf]

  • Yuntao Liu, Yang Xie, and Ankur Srivastava. ICCD, 2017.

• Defending Deep Neural Networks against Backdoor Attack by Using De-trigger Autoencoder. [pdf]

  • Hyun Kwon. IEEE Access, 2021.

• Defending Backdoor Attacks on Vision Transformer via Patch Processing. [pdf]

  • Khoa D. Doan, Yingjie Lao, Peng Yang, and Ping Li. arXiv, 2022.

• ConFoc: Content-Focus Protection Against Trojan Attacks on Neural Networks. [pdf]

  • Miguel Villarreal-Vasquez, and Bharat Bhargava. arXiv, 2021.

• Model Agnostic Defense against Backdoor Attacks in Machine Learning. [pdf]

  • Sakshi Udeshi, Shanshan Peng, Gerald Woo, Lionell Loh, Louth Rawshan, and Sudipta Chattopadhyay. arXiv, 2019.

Model Reconstruction based Empirical Defense

• Adversarial Unlearning of Backdoors via Implicit Hypergradient. [pdf] [code]

  • Yi Zeng, Si Chen, Won Park, Z. Morley Mao, Ming Jin, and Ruoxi Jia. ICLR, 2022.

• Data-free Backdoor Removal based on Channel Lipschitzness. [pdf] [code]

  • Runkai Zheng, Rongjun Tang, Jianze Li, and Li Liu. ECCV, 2022.

• Eliminating Backdoor Triggers for Deep Neural Networks Using Attention Relation Graph Distillation. [pdf]

  • Jun Xia, Ting Wang, Jieping Ding, Xian Wei, and Mingsong Chen. IJCAI, 2022.

• Adversarial Neuron Pruning Purifies Backdoored Deep Models. [pdf] [code]

  • Dongxian Wu and Yisen Wang. NeurIPS, 2021.

• Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks. [pdf] [code]

  • Yige Li, Xingjun Ma, Nodens Koren, Lingjuan Lyu, Xixiang Lyu, and Bo Li. ICLR, 2021.

• Interpretability-Guided Defense against Backdoor Attacks to Deep Neural Networks. [link]

  • Wei Jiang, Xiangyu Wen, Jinyu Zhan, Xupeng Wang, and Ziwei Song. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2021.

• Boundary augment: A data augment method to defend poison attack. [link]

  • Xuan Chen, Yuena Ma, Shiwei Lu, and Yu Yao. IET Image Processing, 2021.

• Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness. [pdf] [code]

  • Pu Zhao, Pin-Yu Chen, Payel Das, Karthikeyan Natesan Ramamurthy, and Xue Lin. ICLR, 2020.

• Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks. [pdf] [code]

  • Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg. RAID, 2018.

• Neural Trojans. [pdf]

  • Yuntao Liu, Yang Xie, and Ankur Srivastava. ICCD, 2017.

• Test-time Adaptation of Residual Blocks against Poisoning and Backdoor Attacks. [pdf]

  • Arnav Gudibande, Xinyun Chen, Yang Bai, Jason Xiong, and Dawn Song. CVPR Workshop, 2022.

• Disabling Backdoor and Identifying Poison Data by using Knowledge Distillation in Backdoor Attacks on Deep Neural Networks. [pdf]

  • Kota Yoshida, and Takeshi Fujino. CCS Workshop, 2020.

• Defending against Backdoor Attack on Deep Neural Networks. [pdf]

  • Hao Cheng, Kaidi Xu, Sijia Liu, Pin-Yu Chen, Pu Zhao, and Xue Lin. KDD Workshop, 2019.

• Defense against Backdoor Attacks via Identifying and Purifying Bad Neurons. [pdf]

  • Mingyuan Fan, Yang Liu, Cen Chen, Ximeng Liu, and Wenzhong Guo. arXiv, 2022.

• Turning a Curse Into a Blessing: Enabling Clean-Data-Free Defenses by Model Inversion. [pdf]

  • Si Chen, Yi Zeng, Won Park, and Ruoxi Jia. arXiv, 2022.

• Adversarial Fine-tuning for Backdoor Defense: Connect Adversarial Examples to Triggered Samples. [pdf]

  • Bingxu Mu, Le Wang, and Zhenxing Niu. arXiv, 2022.

• Neural Network Laundering: Removing Black-Box Backdoor Watermarks from Deep Neural Networks. [pdf]

  • William Aiken, Hyoungshick Kim, and Simon Woo. arXiv, 2020.

• HaS-Nets: A Heal and Select Mechanism to Defend DNNs Against Backdoor Attacks for Data Collection Scenarios. [pdf]

  • Hassan Ali, Surya Nepal, Salil S. Kanhere, and Sanjay Jha. arXiv, 2020.

Trigger Synthesis based Empirical Defense

• Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free. [pdf] [code]

  • Tianlong Chen, Zhenyu Zhang, Yihua Zhang*, Shiyu Chang, Sijia Liu, and Zhangyang Wang. CVPR, 2022.

• Better Trigger Inversion Optimization in Backdoor Scanning. [pdf] [code]

  • Guanhong Tao, Guangyu Shen, Yingqi Liu, Shengwei An, Qiuling Xu, Shiqing Ma, Pan Li, and Xiangyu Zhang. CVPR, 2022.

• Few-shot Backdoor Defense Using Shapley Estimation. [pdf]

  • Jiyang Guan, Zhuozhuo Tu, Ran He, and Dacheng Tao. CVPR, 2022.

• AEVA: Black-box Backdoor Detection Using Adversarial Extreme Value Analysis. [pdf] [code]

  • Junfeng Guo, Ang Li, and Cong Liu. ICLR, 2022.

• Trigger Hunting with a Topological Prior for Trojan Detection. [pdf] [code]

  • Xiaoling Hu, Xiao Lin, Michael Cogswell, Yi Yao, Susmit Jha, and Chao Chen. ICLR, 2022.

• Backdoor Defense with Machine Unlearning. [pdf]

  • Yang Liu, Mingyuan Fan, Cen Chen, Ximeng Liu, Zhuo Ma, Li Wang, and Jianfeng Ma. INFOCOM, 2022.

• Black-box Detection of Backdoor Attacks with Limited Information and Data. [pdf]

  • Yinpeng Dong, Xiao Yang, Zhijie Deng, Tianyu Pang, Zihao Xiao, Hang Su, and Jun Zhu. ICCV, 2021.

• Backdoor Scanning for Deep Neural Networks through K-Arm Optimization. [pdf] [code]

  • Guangyu Shen, Yingqi Liu, Guanhong Tao, Shengwei An, Qiuling Xu, Siyuan Cheng, Shiqing Ma, and Xiangyu Zhang. ICML, 2021.

• Towards Inspecting and Eliminating Trojan Backdoors in Deep Neural Networks. [pdf] [previous version] [code]

  • Wenbo Guo, Lun Wang, Xinyu Xing, Min Du, and Dawn Song. ICDM, 2020.

• GangSweep: Sweep out Neural Backdoors by GAN. [pdf]

  • Liuwan Zhu, Rui Ning, Cong Wang, Chunsheng Xin, and Hongyi Wu. ACM MM, 2020.

• Detection of Backdoors in Trained Classifiers Without Access to the Training Set. [pdf]

  • Z Xiang, DJ Miller, and G Kesidis. IEEE Transactions on Neural Networks and Learning Systems, 2020.

• Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks. [pdf] [code]

  • Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, Ben Y. Zhao. IEEE S&P, 2019.

• Defending Neural Backdoors via Generative Distribution Modeling. [pdf] [code]

  • Ximing Qiao, Yukun Yang, and Hai Li. NeurIPS, 2019.

• DeepInspect: A Black-box Trojan Detection and Mitigation Framework for Deep Neural Networks. [pdf]

  • Huili Chen, Cheng Fu, Jishen Zhao, Farinaz Koushanfar. IJCAI, 2019.

• Identifying Physically Realizable Triggers for Backdoored Face Recognition Networks. [link]

  • Ankita Raj, Ambar Pal, and Chetan Arora. ICIP, 2021.

• Revealing Perceptible Backdoors in DNNs Without the Training Set via the Maximum Achievable Misclassification Fraction Statistic. [pdf]

  • Zhen Xiang, David J. Miller, Hang Wang, and George Kesidis. MLSP, 2020.

• Adaptive Perturbation Generation for Multiple Backdoors Detection. [pdf]

  • Yuhang Wang, Huafeng Shi, Rui Min, Ruijia Wu, Siyuan Liang, Yichao Wu, Ding Liang, and Aishan Liu. arXiv, 2022.

• Confidence Matters: Inspecting Backdoors in Deep Neural Networks via Distribution Transfer. [pdf]

  • Tong Wang, Yuan Yao, Feng Xu, Miao Xu, Shengwei An, and Ting Wang. arXiv, 2022.

• One-shot Neural Backdoor Erasing via Adversarial Weight Masking. [pdf]

  • Shuwen Chai and Jinghui Chen. arXiv, 2022.

• Defense Against Multi-target Trojan Attacks. [pdf]

  • Haripriya Harikumar, Santu Rana, Kien Do, Sunil Gupta, Wei Zong, Willy Susilo, and Svetha Venkastesh. arXiv, 2022.

• Model-Contrastive Learning for Backdoor Defense. [pdf]

  • Zhihao Yue, Jun Xia, Zhiwei Ling, Ting Wang, Xian Wei, and Mingsong Chen. arXiv, 2022.

• CatchBackdoor: Backdoor Testing by Critical Trojan Neural Path Identification via Differential Fuzzing. [pdf]

  • Haibo Jin, Ruoxi Chen, Jinyin Chen, Yao Cheng, Chong Fu, Ting Wang, Yue Yu, and Zhaoyan Ming. arXiv, 2021.

• Detect and Remove Watermark in Deep Neural Networks via Generative Adversarial Networks. [pdf]

  • Haoqi Wang, Mingfu Xue, Shichang Sun, Yushu Zhang, Jian Wang, and Weiqiang Liu. arXiv, 2021.

• TAD: Trigger Approximation based Black-box Trojan Detection for AI. [pdf]

  • Xinqiao Zhang, Huili Chen, and Farinaz Koushanfar. arXiv, 2021.

• Scalable Backdoor Detection in Neural Networks. [pdf]

  • Haripriya Harikumar, Vuong Le, Santu Rana, Sourangshu Bhattacharya, Sunil Gupta, and Svetha Venkatesh. arXiv, 2020.

• NNoculation: Broad Spectrum and Targeted Treatment of Backdoored DNNs. [pdf] [code]

  • Akshaj Kumar Veldanda, Kang Liu, Benjamin Tan, Prashanth Krishnamurthy, Farshad Khorrami, Ramesh Karri, Brendan Dolan-Gavitt, and Siddharth Garg. arXiv, 2020.

Model Diagnosis based Empirical Defense

• Complex Backdoor Detection by Symmetric Feature Differencing. [pdf] [code]

  • Yingqi Liu, Guangyu Shen, Guanhong Tao, Zhenting Wang, Shiqing Ma, and Xiangyu Zhang. CVPR, 2022.

• Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios. [pdf] [code]

  • Zhen Xiang, David J. Miller, and George Kesidis. ICLR, 2022.

• An Anomaly Detection Approach for Backdoored Neural Networks: Face Recognition as a Case Study. [pdf]

  • Alexander Unnervik and Sébastien Marcel. BIOSIG, 2022.

• Critical Path-Based Backdoor Detection for Deep Neural Networks. [link]

  • Wei Jiang, Xiangyu Wen, Jinyu Zhan, Xupeng Wang, Ziwei Song, and Chen Bian. IEEE Transactions on Neural Networks and Learning Systems, 2022.

• Detecting AI Trojans Using Meta Neural Analysis. [pdf]

  • Xiaojun Xu, Qi Wang, Huichen Li, Nikita Borisov, Carl A. Gunter, and Bo Li. IEEE S&P, 2021.

• Topological Detection of Trojaned Neural Networks. [pdf]

  • Songzhu Zheng, Yikai Zhang, Hubert Wagner, Mayank Goswami, and Chao Chen. NeurIPS, 2021.

• Black-box Detection of Backdoor Attacks with Limited Information and Data. [pdf]

  • Yinpeng Dong, Xiao Yang, Zhijie Deng, Tianyu Pang, Zihao Xiao, Hang Su, and Jun Zhu. ICCV, 2021.

• Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs. [pdf] [code]

  • Soheil Kolouri, Aniruddha Saha, Hamed Pirsiavash, and Heiko Hoffmann. CVPR, 2020.

• One-Pixel Signature: Characterizing CNN Models for Backdoor Detection. [pdf]

  • Shanjiaoyang Huang, Weiqi Peng, Zhiwei Jia, and Zhuowen Tu. ECCV, 2020.

• Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases. [pdf] [code]

  • Ren Wang, Gaoyuan Zhang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong, and Meng Wang. ECCV, 2020.

• Detecting Backdoor Attacks via Class Difference in Deep Neural Networks. [pdf]

  • Hyun Kwon. IEEE Access, 2020.

• Baseline Pruning-Based Approach to Trojan Detection in Neural Networks. [pdf]

  • Peter Bajcsy and Michael Majurski. ICLR Workshop, 2021.

• Attention Hijacking in Trojan Transformers. [pdf]

  • Weimin Lyu, Songzhu Zheng, Tengfei Ma, Haibin Ling, and Chao Chen. arXiv, 2022.

• Universal Post-Training Backdoor Detection. [pdf]

  • Hang Wang, Zhen Xiang, David J. Miller, and George Kesidis. arXiv, 2022.

• Trojan Signatures in DNN Weights. [pdf]

  • Greg Fields, Mohammad Samragh, Mojan Javaheripi, Farinaz Koushanfar, and Tara Javidi. arXiv, 2021.

• EX-RAY: Distinguishing Injected Backdoor from Natural Features in Neural Networks by Examining Differential Feature Symmetry. [pdf]

  • Yingqi Liu, Guangyu Shen, Guanhong Tao, Zhenting Wang, Shiqing Ma, and Xiangyu Zhang. arXiv, 2021.

• TOP: Backdoor Detection in Neural Networks via Transferability of Perturbation. [pdf]

  • Todd Huster and Emmanuel Ekwedike. arXiv, 2021.

• Detecting Trojaned DNNs Using Counterfactual Attributions. [pdf]

  • Karan Sikka, Indranil Sur, Susmit Jha, Anirban Roy, and Ajay Divakaran. arXiv, 2021.

• Adversarial examples are useful too! [pdf] [code]

  • XBorji A. arXiv, 2020.

• Cassandra: Detecting Trojaned Networks from Adversarial Perturbations. [pdf]

  • Xiaoyu Zhang, Ajmal Mian, Rohit Gupta, Nazanin Rahnavard, and Mubarak Shah. arXiv, 2020.

• Odyssey: Creation, Analysis and Detection of Trojan Models. [pdf] [dataset]

  • Marzieh Edraki, Nazmul Karim, Nazanin Rahnavard, Ajmal Mian, and Mubarak Shah. arXiv, 2020.

• Noise-response Analysis for Rapid Detection of Backdoors in Deep Neural Networks. [pdf]

  • N. Benjamin Erichson, Dane Taylor, Qixuan Wu, and Michael W. Mahoney. arXiv, 2020.

• NeuronInspect: Detecting Backdoors in Neural Networks via Output Explanations. [pdf]

  • Xijie Huang, Moustafa Alzantot, and Mani Srivastava. arXiv, 2019.

Poison Suppression based Empirical Defense

• Backdoor Defense via Decoupling the Training Process. [pdf] [code]

  • Kunzhe Huang, Yiming Li, Baoyuan Wu, Zhan Qin, and Kui Ren. ICLR, 2022.

• Training with More Confidence: Mitigating Injected and Natural Backdoors During Training. [pdf] [code]

  • Zhenting Wang, Zhenting_Wang, Hailun Ding, Juan Zhai, and Shiqing Ma. NeurIPS, 2022.

• Anti-Backdoor Learning: Training Clean Models on Poisoned Data. [pdf] [code]

  • Yige Li, Xixiang Lyu, Nodens Koren, Lingjuan Lyu, Bo Li, and Xingjun Ma. NeurIPS, 2021.

• Robust Anomaly Detection and Backdoor Attack Detection via Differential Privacy. [pdf] [code]

  • Min Du, Ruoxi Jia, and Dawn Song. ICLR, 2020.

• Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Trade-off. [pdf]

  • Eitan Borgnia, Valeriia Cherepanova, Liam Fowl, Amin Ghiasi, Jonas Geiping, Micah Goldblum, Tom Goldstein, and Arjun Gupta. ICASSP, 2021.

• What Doesn't Kill You Makes You Robust(er): Adversarial Training against Poisons and Backdoors. [pdf]

  • Jonas Geiping, Liam Fowl, Gowthami Somepalli, Micah Goldblum, Michael Moeller, and Tom Goldstein. ICLR Workshop, 2021.

• Removing Backdoor-Based Watermarks in Neural Networks with Limited Data. [pdf]

  • Xuankai Liu, Fengting Li, Bihan Wen, and Qi Li. ICPR, 2021.

• On the Effectiveness of Adversarial Training against Backdoor Attacks. [pdf]

  • Yinghua Gao, Dongxian Wu, Jingfeng Zhang, Guanhao Gan, Shu-Tao Xia, Gang Niu, and Masashi Sugiyama. arXiv, 2022.

• Resurrecting Trust in Facial Recognition: Mitigating Backdoor Attacks in Face Recognition to Prevent Potential Privacy Breaches. [pdf]

  • Reena Zelenkova, Jack Swallow, M. A. P. Chamikara, Dongxi Liu, Mohan Baruwal Chhetri, Seyit Camtepe, Marthie Grobler, and Mahathir Almashor. arXiv, 2022.

• SanitAIs: Unsupervised Data Augmentation to Sanitize Trojaned Neural Networks. [pdf]

  • Kiran Karra and Chace Ashcraft. arXiv, 2021.

• On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping. [pdf] [code]

  • Sanghyun Hong, Varun Chandrasekaran, Yiğitcan Kaya, Tudor Dumitraş, and Nicolas Papernot. arXiv, 2020.

• DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations. [pdf]

  • Eitan Borgnia, Jonas Geiping, Valeriia Cherepanova, Liam Fowl, Arjun Gupta, Amin Ghiasi, Furong Huang, Micah Goldblum, and Tom Goldstein. arXiv, 2021.

Sample Filtering based Empirical Defense

• The "Beatrix'' Resurrections: Robust Backdoor Detection via Gram Matrices. [pdf] [code]

  • Wanlun Ma, Derui Wang, Ruoxi Sun, Minhui Xue, Sheng Wen, and Yang Xiang. NDSS, 2023.

• Towards Effective and Robust Neural Trojan Defenses via Input Filtering. [pdf] [code]

  • Kien Do, Haripriya Harikumar, Hung Le, Dung Nguyen, Truyen Tran, Santu Rana, Dang Nguyen, Willy Susilo, and Svetha Venkatesh. ECCV, 2022.

• Effective Backdoor Defense by Exploiting Sensitivity of Poisoned Samples. [pdf] [code]

  • Weixin Chen, Baoyuan Wu, and Haoqian Wang. NeurIPS, 2022.

• Can We Mitigate Backdoor Attack Using Adversarial Detection Methods? [link]

  • Kaidi Jin, Tianwei Zhang, Chao Shen, Yufei Chen, Ming Fan, Chenhao Lin, and Ting Liu. IEEE Transactions on Dependable and Secure Computing, 2022.

• LinkBreaker: Breaking the Backdoor-Trigger Link in DNNs via Neurons Consistency Check. [link]

  • Zhenzhu Chen, Shang Wang, Anmin Fu, Yansong Gao, Shui Yu, and Robert H. Deng. IEEE Transactions on Information Forensics and Security, 2022.

• Similarity-based Integrity Protection for Deep Learning Systems. [link]

  • Ruitao Hou, Shan Ai, Qi Chen, Hongyang Yan, Teng Huang, and Kongyang Chen. Information Sciences, 2022.

• A Feature-Based On-Line Detector to Remove Adversarial-Backdoors by Iterative Demarcation. [pdf]

  • Hao Fu, Akshaj Kumar Veldanda, Prashanth Krishnamurthy, Siddharth Garg, and Farshad Khorrami. IEEE ACCESS, 2022.

• Rethinking the Backdoor Attacks' Triggers: A Frequency Perspective. [pdf] [code]

  • Yi Zeng, Won Park, Z. Morley Mao, and Ruoxi Jia. ICCV, 2021.

• Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection. [pdf] [code]

  • Di Tang, XiaoFeng Wang, Haixu Tang, and Kehuan Zhang. USENIX Security, 2021.

• SPECTRE: Defending Against Backdoor Attacks Using Robust Statistics. [pdf] [code]

  • Jonathan Hayase, Weihao Kong, Raghav Somani, and Sewoong Oh. ICML, 2021.

• CLEANN: Accelerated Trojan Shield for Embedded Neural Networks. [pdf]

  • Mojan Javaheripi, Mohammad Samragh, Gregory Fields, Tara Javidi, and Farinaz Koushanfar. ICCAD, 2020.

• Robust Anomaly Detection and Backdoor Attack Detection via Differential Privacy. [pdf] [code]

  • Min Du, Ruoxi Jia, and Dawn Song. ICLR, 2020.

• Simple, Attack-Agnostic Defense Against Targeted Training Set Attacks Using Cosine Similarity. [pdf] [code]

  • Zayd Hammoudeh and Daniel Lowd. ICML Workshop, 2021.

• SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems. [pdf]

  • Edward Chou, Florian Tramèr, and Giancarlo Pellegrino. IEEE S&P Workshop, 2020.

• STRIP: A Defence Against Trojan Attacks on Deep Neural Networks. [pdf] [extension] [code]

  • Yansong Gao, Chang Xu, Derui Wang, Shiping Chen, Damith C. Ranasinghe, and Surya Nepal. ACSAC, 2019.

• Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering. [pdf] [code]

  • Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Benjamin Edwards, Taesung Lee, Ian Molloy, and Biplav Srivastava. AAAI Workshop, 2019.

• Deep Probabilistic Models to Detect Data Poisoning Attacks. [pdf]

  • Mahesh Subedar, Nilesh Ahuja, Ranganath Krishnan, Ibrahima J. Ndiour, and Omesh Tickoo. NeurIPS Workshop, 2019.

• Spectral Signatures in Backdoor Attacks. [pdf] [code]

  • Brandon Tran, Jerry Li, and Aleksander Madry. NeurIPS, 2018.

• An Adaptive Black-box Defense against Trojan Attacks (TrojDef). [pdf]

  • Guanxiong Liu, Abdallah Khreishah, Fatima Sharadgah, and Issa Khalil. arXiv, 2022.

• Fight Poison with Poison: Detecting Backdoor Poison Samples via Decoupling Benign Correlations. [pdf] [code]

  • Xiangyu Qi, Tinghao Xie, Saeed Mahloujifar, and Prateek Mittal. arXiv, 2022.

• PiDAn: A Coherence Optimization Approach for Backdoor Attack Detection and Mitigation in Deep Neural Networks. [pdf]

  • Yue Wang, Wenqing Li, Esha Sarkar, Muhammad Shafique, Michail Maniatakos, and Saif Eddin Jabari. arXiv, 2022.

• Neural Network Trojans Analysis and Mitigation from the Input Domain. [pdf]

  • Zhenting Wang, Hailun Ding, Juan Zhai, and Shiqing Ma. arXiv, 2022.

• A General Framework for Defending Against Backdoor Attacks via Influence Graph. [pdf]

  • Xiaofei Sun, Jiwei Li, Xiaoya Li, Ziyao Wang, Tianwei Zhang, Han Qiu, Fei Wu, and Chun Fan. arXiv, 2021.

• NTD: Non-Transferability Enabled Backdoor Detection. [pdf]

  • Yinshan Li, Hua Ma, Zhi Zhang, Yansong Gao, Alsharif Abuadbba, Anmin Fu, Yifeng Zheng, Said F. Al-Sarawi, and Derek Abbott. arXiv, 2021.

• A Unified Framework for Task-Driven Data Quality Management. [pdf]

  • Tianhao Wang, Yi Zeng, Ming Jin, and Ruoxi Jia. arXiv, 2021.

• TESDA: Transform Enabled Statistical Detection of Attacks in Deep Neural Networks. [pdf]

  • Chandramouli Amarnath, Aishwarya H. Balwani, Kwondo Ma, and Abhijit Chatterjee. arXiv, 2021.

• Traceback of Data Poisoning Attacks in Neural Networks. [pdf]

  • Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng, and Ben Y. Zhao. arXiv, 2021.

• Provable Guarantees against Data Poisoning Using Self-Expansion and Compatibility. [pdf]

  • Charles Jin, Melinda Sun, and Martin Rinard. arXiv, 2021.

• Online Defense of Trojaned Models using Misattributions. [pdf]

  • Panagiota Kiourti, Wenchao Li, Anirban Roy, Karan Sikka, and Susmit Jha. arXiv, 2021.

• Detecting Backdoor in Deep Neural Networks via Intentional Adversarial Perturbations. [pdf]

  • Mingfu Xue, Yinghao Wu, Zhiyu Wu, Jian Wang, Yushu Zhang, and Weiqiang Liu. arXiv, 2021.

• Exposing Backdoors in Robust Machine Learning Models. [pdf]

  • Ezekiel Soremekun, Sakshi Udeshi, and Sudipta Chattopadhyay. arXiv, 2020.

• HaS-Nets: A Heal and Select Mechanism to Defend DNNs Against Backdoor Attacks for Data Collection Scenarios. [pdf]

  • Hassan Ali, Surya Nepal, Salil S. Kanhere, and Sanjay Jha. arXiv, 2020.

• Poison as a Cure: Detecting & Neutralizing Variable-Sized Backdoor Attacks in Deep Neural Networks. [pdf]

  • Alvin Chan, and Yew-Soon Ong. arXiv, 2019.

Certificated Defense

• BagFlip: A Certified Defense against Data Poisoning. [pdf] [code]

  • Yuhao Zhang, Aws Albarghouthi, and Loris D'Antoni. NeurIPS, 2022.

• RAB: Provable Robustness Against Backdoor Attacks. [pdf] [code]

  • Maurice Weber, Xiaojun Xu, Bojan Karlas, Ce Zhang, and Bo Li. IEEE S&P, 2022.

• Certified Robustness of Nearest Neighbors against Data Poisoning and Backdoor Attacks. [pdf]

  • Jinyuan Jia, Yupei Liu, Xiaoyu Cao, and Neil Zhenqiang Gong. AAAI, 2022.

• Deep Partition Aggregation: Provable Defense against General Poisoning Attacks [pdf] [code]

  • Alexander Levine and Soheil Feizi. ICLR, 2021.

• Intrinsic Certified Robustness of Bagging against Data Poisoning Attacks [pdf] [code]

  • Jinyuan Jia, Xiaoyu Cao, and Neil Zhenqiang Gong. AAAI, 2021.

• Certified Robustness to Label-Flipping Attacks via Randomized Smoothing. [pdf]

  • Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar, and J. Zico Kolter. ICML, 2020.

• On Certifying Robustness against Backdoor Attacks via Randomized Smoothing. [pdf]

  • Binghui Wang, Xiaoyu Cao, Jinyuan jia, and Neil Zhenqiang Gong. CVPR Workshop, 2020.

• BagFlip: A Certified Defense against Data Poisoning. [pdf]

  • Yuhao Zhang, Aws Albarghouthi, and Loris D'Antoni. arXiv, 2022.

Attack and Defense Towards Other Paradigms and Tasks

Federated Learning

• Neurotoxin: Durable Backdoors in Federated Learning. [pdf]

  • Zhengming Zhang, Ashwinee Panda, Linyue Song, Yaoqing Yang, Michael W. Mahoney, Joseph E. Gonzalez, Kannan Ramchandran, and Prateek Mittal. ICML, 2022.

• FLAME: Taming Backdoors in Federated Learning. [pdf]

  • Thien Duc Nguyen, Phillip Rieger, Huili Chen, Hossein Yalame, Helen Möllering, Hossein Fereidooni, Samuel Marchal, Markus Miettinen, Azalia Mirhoseini, Shaza Zeitouni, Farinaz Koushanfar, Ahmad-Reza Sadeghi, and Thomas Schneider. USENIX Security, 2022.

• DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection. [pdf]

  • Phillip Rieger, Thien Duc Nguyen, Markus Miettinen, and Ahmad-Reza Sadeghi. NDSS, 2022.

• Defending Label Inference and Backdoor Attacks in Vertical Federated Learning. [pdf]

  • Yang Liu, Zhihao Yi, Yan Kang, Yuanqin He, Wenhan Liu, Tianyuan Zou, and Qiang Yang. AAAI, 2022.

• An Analysis of Byzantine-Tolerant Aggregation Mechanisms on Model Poisoning in Federated Learning. [link]

  • Mary Roszel, Robert Norvill, and Radu State. MDAI, 2022.

• Against Backdoor Attacks In Federated Learning With Differential Privacy. [link]

  • Lu Miao, Wei Yang, Rong Hu, Lu Li, and Liusheng Huang. ICASSP, 2022.

• Secure Partial Aggregation: Making Federated Learning More Robust for Industry 4.0 Applications. [link]

  • Jiqiang Gao, Baolei Zhang, Xiaojie Guo, Thar Baker, Min Li, and Zheli Liu. IEEE Transactions on Industrial Informatics, 2022.

• Backdoor Attacks-resilient Aggregation based on Robust Filtering of Outliers in Federated Learning for Image Classification. [link]

  • Nuria Rodríguez-Barroso, Eugenio Martínez-Cámara, M. Victoria Luzónb, and Francisco Herrera. Knowledge-Based Systems, 2022.

• Defense against Backdoor Attack in Federated Learning. [link] [code]

  • Shiwei Lu, Ruihu Li, Wenbin Liu, and Xuan Chen. Computers & Security, 2022.

• Privacy-Enhanced Federated Learning against Poisoning Adversaries. [link]

  • Xiaoyuan Liu, Hongwei Li, Guowen Xu, Zongqi Chen, Xiaoming Huang, and Rongxing Lu. IEEE Transactions on Information Forensics and Security, 2021.

• Coordinated Backdoor Attacks against Federated Learning with Model-Dependent Triggers. [link]

  • Xueluan Gong, Yanjiao Chen, Huayang Huang, Yuqing Liao, Shuai Wang, and Qian Wang. IEEE Network, 2022.

• CRFL: Certifiably Robust Federated Learning against Backdoor Attacks. [pdf]

  • Chulin Xie, Minghao Chen, Pin-Yu Chen, and Bo Li. ICML, 2021.

• Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning. [pdf]

  • Syed Zawad, Ahsan Ali, Pin-Yu Chen, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Yuan Tian, and Feng Yan. AAAI, 2021.

• Defending Against Backdoors in Federated Learning with Robust Learning Rate. [pdf]

  • Mustafa Safa Ozdayi, Murat Kantarcioglu, and Yulia R. Gel. AAAI, 2021.

• BaFFLe: Backdoor detection via Feedback-based Federated Learning. [pdf]

  • ebastien Andreina, Giorgia Azzurra Marson, Helen Möllering, and Ghassan Karame. ICDCS, 2021.

• PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion. [pdf]

  • Shijie Zhang, Hongzhi Yin, Tong Chen, Zi Huang, Quoc Viet Hung Nguyen, and Lizhen Cui. WSDM, 2021.

• Mitigating the Backdoor Attack by Federated Filters for Industrial IoT Applications. [link]

  • Boyu Hou, Jiqiang Gao, Xiaojie Guo, Thar Baker, Ying Zhang, Yanlong Wen, and Zheli Liu. IEEE Transactions on Industrial Informatics, 2021.

• Stability-Based Analysis and Defense against Backdoor Attacks on Edge Computing Services. [link]

  • Yi Zhao, Ke Xu, Haiyang Wang, Bo Li, and Ruoxi Jia. IEEE Network, 2021.

• Attack of the Tails: Yes, You Really Can Backdoor Federated Learning. [pdf]

  • Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, and Dimitris Papailiopoulos. NeurIPS, 2020.

• DBA: Distributed Backdoor Attacks against Federated Learning. [pdf]

  • Chulin Xie, Keli Huang, Pinyu Chen, and Bo Li. ICLR, 2020.

• The Limitations of Federated Learning in Sybil Settings. [pdf] [extension] [code]

  • Clement Fung, Chris J.M. Yoon, and Ivan Beschastnikh. RAID, 2020 (arXiv, 2018).

• How to Backdoor Federated Learning. [pdf]

  • Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. AISTATS, 2020 (arXiv, 2018).

• BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning. [pdf]

  • Arup Mondal, Harpreet Virk, and Debayan Gupta. AAAI Workshop, 2022.

• Backdoor Attacks and Defenses in Feature-partitioned Collaborative Learning. [pdf]

  • Yang Liu, Zhihao Yi, and Tianjian Chen. ICML Workshop, 2020.

• Can You Really Backdoor Federated Learning? [pdf]

  • Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H. Brendan McMahan. NeurIPS Workshop, 2019.

• Invariant Aggregator for Defending Federated Backdoor Attacks. [pdf]

  • Xiaoyang Wang, Dimitrios Dimitriadis, Sanmi Koyejo, and Shruti Tople. arXiv, 2022.

• Shielding Federated Learning: Mitigating Byzantine Attacks with Less Constraints. [pdf]

  • Minghui Li, Wei Wan, Jianrong Lu, Shengshan Hu, Junyu Shi, and Leo Yu Zhang. arXiv, 2022.

• Federated Zero-Shot Learning for Visual Recognition. [pdf]

  • Zhi Chen, Yadan Luo, Sen Wang, Jingjing Li, and Zi Huang. arXiv, 2022.

• Assisting Backdoor Federated Learning with Whole Population Knowledge Alignment. [pdf]

  • Tian Liu, Xueyang Hu, and Tao Shu. arXiv, 2022.

• FL-Defender: Combating Targeted Attacks in Federated Learning. [pdf]

  • Najeeb Jebreel and Josep Domingo-Ferrer. arXiv, 2022.

• Backdoor Attack is A Devil in Federated GAN-based Medical Image Synthesis. [pdf]

  • Ruinan Jin and Xiaoxiao Li. arXiv, 2022.

• SafeNet: Mitigating Data Poisoning Attacks on Private Machine Learning. [pdf] [code]

  • Harsh Chaudhari, Matthew Jagielski, and Alina Oprea. arXiv, 2022.

• PerDoor: Persistent Non-Uniform Backdoors in Federated Learning using Adversarial Perturbations. [pdf] [code]

  • Manaar Alam, Esha Sarkar, and Michail Maniatakos. arXiv, 2022.

• Towards a Defense against Backdoor Attacks in Continual Federated Learning. [pdf]

  • Shuaiqi Wang, Jonathan Hayase, Giulia Fanti, and Sewoong Oh. arXiv, 2022.

• Client-Wise Targeted Backdoor in Federated Learning. [pdf]

  • Gorka Abad, Servio Paguada, Stjepan Picek, Víctor Julio Ramírez-Durán, and Aitor Urbieta. arXiv, 2022.

• Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection. [pdf]

  • Yein Kim, Huili Chen, and Farinaz Koushanfar. arXiv, 2022.

• ARIBA: Towards Accurate and Robust Identification of Backdoor Attacks in Federated Learning. [pdf]

  • Yuxi Mi, Jihong Guan, and Shuigeng Zhou. arXiv, 2022.

• More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks. [pdf]

  • Jing Xu, Rui Wang, Kaitai Liang, and Stjepan Picek. arXiv, 2022.

• Low-Loss Subspace Compression for Clean Gains against Multi-Agent Backdoor Attacks. [pdf]

  • Siddhartha Datta and Nigel Shadbolt. arXiv, 2022.

• Backdoors Stuck at The Frontdoor: Multi-Agent Backdoor Attacks That Backfire. [pdf]

  • Siddhartha Datta and Nigel Shadbolt. arXiv, 2022.

• Federated Unlearning with Knowledge Distillation. [pdf]

  • Chen Wu, Sencun Zhu, and Prasenjit Mitra. arXiv, 2022.

• Model Transferring Attacks to Backdoor HyperNetwork in Personalized Federated Learning. [pdf]

  • Phung Lai, NhatHai Phan, Abdallah Khreishah, Issa Khalil, and Xintao Wu. arXiv, 2022.

• Backdoor Attacks on Federated Learning with Lottery Ticket Hypothesis. [pdf]

  • Zihang Zou, Boqing Gong, and Liqiang Wang. arXiv, 2021.

• On Provable Backdoor Defense in Collaborative Learning. [pdf]

  • Ximing Qiao, Yuhua Bai, Siping Hu, Ang Li, Yiran Chen, and Hai Li. arXiv, 2021.

• SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification. [pdf]

  • Ashwinee Panda, Saeed Mahloujifar, Arjun N. Bhagoji, Supriyo Chakraborty, and Prateek Mittal. arXiv, 2021.

• Robust Federated Learning with Attack-Adaptive Aggregation. [pdf] [code]

  • Ching Pui Wan, and Qifeng Chen. arXiv, 2021.

• Meta Federated Learning. [pdf]

  • Omid Aramoon, Pin-Yu Chen, Gang Qu, and Yuan Tian. arXiv, 2021.

• FLGUARD: Secure and Private Federated Learning. [pdf]

  • Thien Duc Nguyen, Phillip Rieger, Hossein Yalame, Helen Möllering, Hossein Fereidooni, Samuel Marchal, Markus Miettinen, Azalia Mirhoseini, Ahmad-Reza Sadeghi, Thomas Schneider, and Shaza Zeitouni. arXiv, 2021.

• Toward Robustness and Privacy in Federated Learning: Experimenting with Local and Central Differential Privacy. [pdf]

  • Mohammad Naseri, Jamie Hayes, and Emiliano De Cristofaro. arXiv, 2020.

• Backdoor Attacks on Federated Meta-Learning. [pdf]

  • Chien-Lun Chen, Leana Golubchik, and Marco Paolieri. arXiv, 2020.

• Dynamic backdoor attacks against federated learning. [pdf]

  • Anbu Huang. arXiv, 2020.

• Federated Learning in Adversarial Settings. [pdf]

  • Raouf Kerkouche, Gergely Ács, and Claude Castelluccia. arXiv, 2020.

• BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecture. [pdf]

  • Harsh Bimal Desai, Mustafa Safa Ozdayi, and Murat Kantarcioglu. arXiv, 2020.

• Mitigating Backdoor Attacks in Federated Learning. [pdf]

  • Chen Wu, Xian Yang, Sencun Zhu, and Prasenjit Mitra. arXiv, 2020.

• Learning to Detect Malicious Clients for Robust Federated Learning. [pdf]

  • Suyi Li, Yong Cheng, Wei Wang, Yang Liu, and Tianjian Chen. arXiv, 2020.

• Attack-Resistant Federated Learning with Residual-based Reweighting. [pdf] [code]

  • Shuhao Fu, Chulin Xie, Bo Li, and Qifeng Chen. arXiv, 2019.

Transfer Learning

• Incremental Learning, Incremental Backdoor Threats. [link]

  • Wenbo Jiang, Tianwei Zhang, Han Qiu, Hongwei Li, and Guowen Xu. IEEE Transactions on Dependable and Secure Computing, 2022.

• Robust Backdoor Injection with the Capability of Resisting Network Transfer. [link]

  • Le Feng, Sheng Li, Zhenxing Qian, and Xinpeng Zhang. Information Sciences, 2022.

• Anti-Distillation Backdoor Attacks: Backdoors Can Really Survive in Knowledge Distillation. [pdf]

  • Yunjie Ge, Qian Wang, Baolin Zheng, Xinlu Zhuang, Qi Li, Chao Shen, and Cong Wang. ACM MM, 2021.

• Hidden Trigger Backdoor Attacks. [pdf] [code]

  • Aniruddha Saha, Akshayvarun Subramanya, and Hamed Pirsiavash. AAAI, 2020.

• Weight Poisoning Attacks on Pre-trained Models. [pdf] [code]

  • Keita Kurita, Paul Michel, and Graham Neubig. ACL, 2020.

• Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models. [pdf]

  • Shuo Wang, Surya Nepal, Carsten Rudolph, Marthie Grobler, Shangyu Chen, and Tianle Chen. IEEE Transactions on Services Computing, 2020.

• Latent Backdoor Attacks on Deep Neural Networks. [pdf]

  • Yuanshun Yao, Huiying Li, Haitao Zheng and Ben Y. Zhao. CCS, 2019.

• Architectural Backdoors in Neural Networks. [pdf]

  • Mikel Bober-Irizar, Ilia Shumailov, Yiren Zhao, Robert Mullins, and Nicolas Papernot. arXiv, 2022.

• Red Alarm for Pre-trained Models: Universal Vulnerabilities by Neuron-Level Backdoor Attacks. [pdf] [code]

  • Zhengyan Zhang, Guangxuan Xiao, Yongwei Li, Tian Lv, Fanchao Qi, Zhiyuan Liu, Yasheng Wang, Xin Jiang, and Maosong Sun. arXiv, 2021.

Reinforcement Learning

• Provable Defense against Backdoor Policies in Reinforcement Learning. [pdf] [code]

  • Shubham Kumar Bharti, Xuezhou Zhang, Adish Singla, and Jerry Zhu. NeurIPS, 2022.

• MARNet: Backdoor Attacks against Cooperative Multi-Agent Reinforcement Learning. [link]

  • Yanjiao Chen, Zhicong Zheng, and Xueluan Gong. IEEE Transactions on Dependable and Secure Computing, 2022.

• BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning. [pdf]

  • Lun Wang, Zaynah Javed, Xian Wu, Wenbo Guo, Xinyu Xing, and Dawn Song. IJCAI, 2021.

• Stop-and-Go: Exploring Backdoor Attacks on Deep Reinforcement Learning-based Traffic Congestion Control Systems. [pdf]

  • Yue Wang, Esha Sarkar, Michail Maniatakos, and Saif Eddin Jabari. IEEE Transactions on Information Forensics and Security, 2021.

• Agent Manipulator: Stealthy Strategy Attacks on Deep Reinforcement Learning. [link]

  • Jinyin Chen, Xueke Wang, Yan Zhang, Haibin Zheng, Shanqing Yu, and Liang Bao. Applied Intelligence, 2022.

• TrojDRL: Evaluation of Backdoor Attacks on Deep Reinforcement Learning. [pdf] [code]

  • Panagiota Kiourti, Kacper Wardega, Susmit Jha, and Wenchao Li. DAC, 2020.

• Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers. [pdf]

  • Chace Ashcraft and Kiran Karra. ICLR Workshop, 2021.

• A Temporal-Pattern Backdoor Attack to Deep Reinforcement Learning. [pdf]

  • Yinbo Yu, Jiajia Liu, Shouqing Li, Kepu Huang, and Xudong Feng. arXiv, 2022.

• Backdoor Detection in Reinforcement Learning. [pdf]

  • Junfeng Guo, Ang Li, and Cong Liu. arXiv, 2022.

• Design of Intentional Backdoors in Sequential Models. [pdf]

  • Zhaoyuan Yang, Naresh Iyer, Johan Reimann, and Nurali Virani. arXiv, 2019.

Semi-Supervised and Self-Supervised Learning

• Backdoor Attacks on Self-Supervised Learning. [pdf] [code]

  • Aniruddha Saha, Ajinkya Tejankar, Soroush Abbasi Koohpayegani, and Hamed Pirsiavash. CVPR, 2022.

• Poisoning and Backdooring Contrastive Learning. [pdf]

  • Nicholas Carlini and Andreas Terzis. ICLR, 2022.

• BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised Learning. [pdf] [code]

  • Jinyuan Jia, Yupei Liu, and Neil Zhenqiang Gong. IEEE S&P, 2022.

• DeHiB: Deep Hidden Backdoor Attack on Semi-supervised Learning via adversarial Perturbation. [pdf]

  • Zhicong Yan, Gaolei Li, Yuan Tian, Jun Wu, Shenghong Li, Mingzhe Chen, and H. Vincent Poor. AAAI, 2021.

• Deep Neural Backdoor in Semi-Supervised Learning: Threats and Countermeasures. [link]

  • Zhicong Yan, Jun Wu, Gaolei Li, Shenghong Li, and Mohsen Guizani. IEEE Transactions on Information Forensics and Security, 2021.

• Backdoor Attacks in the Supply Chain of Masked Image Modeling. [pdf]

  • Xinyue Shen, Xinlei He, Zheng Li, Yun Shen, Michael Backes, and Yang Zhang. arXiv, 2022.

• Watermarking Pre-trained Encoders in Contrastive Learning. [pdf]

  • Yutong Wu, Han Qiu, Tianwei Zhang, Jiwei Li, and Meikang Qiu. arXiv, 2022.

Quantization

• RIBAC: Towards Robust and Imperceptible Backdoor Attack against Compact DNN. [pdf] [code]

  • Huy Phan, Cong Shi, Yi Xie, Tianfang Zhang, Zhuohang Li, Tianming Zhao, Jian Liu, Yan Wang, Yingying Chen, and Bo Yuan. ECCV, 2022.

• Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes. [pdf] [code]

  • Sanghyun Hong, Michael-Andrei Panaitescu-Liess, Yiğitcan Kaya, and Tudor Dumitraş. NeurIPS, 2021.

• Understanding the Threats of Trojaned Quantized Neural Network in Model Supply Chains. [pdf]

  • Xudong Pan, Mi Zhang, Yifan Yan, and Min Yang. ACSAC, 2021

• Quantization Backdoors to Deep Learning Models. [pdf]

  • Hua Ma, Huming Qiu, Yansong Gao, Zhi Zhang, Alsharif Abuadbba, Anmin Fu, Said Al-Sarawi, and Derek Abbott. arXiv, 2021.

• Stealthy Backdoors as Compression Artifacts. [pdf]

  • Yulong Tian, Fnu Suya, Fengyuan Xu, and David Evans. arXiv, 2021.

Natural Language Processing

• BadPrompt: Backdoor Attacks on Continuous Prompts. [pdf] [code]

  • Xiangrui Cai, haidong xu, Sihan Xu, Ying Zhang, and Xiaojie Yuan. NeurIPS, 2022.

• Moderate-fitting as a Natural Backdoor Defender for Pre-trained Language Models [pdf] [code]

  • Biru Zhu, Yujia Qin, Ganqu Cui, Yangyi Chen, Weilin Zhao, Chong Fu, Yangdong Deng, Zhiyuan Liu, Jingang Wang, Wei Wu, Maosong Sun, and Ming Gu. NeurIPS, 2022.

• A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks. [pdf] [code]

  • Ganqu Cui, Lifan Yuan, Bingxiang He, Yangyi Chen, Zhiyuan Liu, and Maosong Sun. NeurIPS, 2022.

• Spinning Language Models: Risks of Propaganda-as-a-Service and Countermeasures [pdf] [code]

  • Eugene Bagdasaryan and Vitaly Shmatikov. IEEE S&P, 2022.

• PICCOLO: Exposing Complex Backdoors in NLP Transformer Models. [pdf] [code]

  • Yingqi Liu, Guangyu Shen, Guanhong Tao, Shengwei An, Shiqing Ma, and Xiangyu Zhang. IEEE S&P, 2022.

• Triggerless Backdoor Attack for NLP Tasks with Clean Labels. [pdf]

  • Leilei Gan, Jiwei Li, Tianwei Zhang, Xiaoya Li, Yuxian Meng, Fei Wu, Shangwei Guo, and Chun Fan. NAACL, 2022.

• A Study of the Attention Abnormality in Trojaned BERTs. [pdf] [code]

  • Weimin Lyu, Songzhu Zheng, Tengfei Ma, and Chao Chen. NAACL, 2022.

• The Triggers that Open the NLP Model Backdoors Are Hidden in the Adversarial Samples. [link]

  • Kun Shao, Yu Zhang, Junan Yang, Xiaoshuai Li, and Hui Liu. Computers & Security, 2022.

• BDDR: An Effective Defense Against Textual Backdoor Attacks. [pdf]

  • Kun Shao, Junan Yang, Yang Ai, Hui Liu, and Yu Zhang. Computers & Security, 2021.

• BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models. [pdf]

  • Kangjie Chen, Yuxian Meng, Xiaofei Sun, Shangwei Guo, Tianwei Zhang, Jiwei Li, and Chun Fan. ICLR, 2022.

• Exploring the Universal Vulnerability of Prompt-based Learning Paradigm. [pdf] [code]

  • Lei Xu, Yangyi Chen, Ganqu Cui, Hongcheng Gao, and Zhiyuan Liu. NAACL-Findings, 2022.

• Backdoor Pre-trained Models Can Transfer to All. [pdf]

  • Lujia Shen, Shouling Ji, Xuhong Zhang, Jinfeng Li, Jing Chen, Jie Shi, Chengfang Fang, Jianwei Yin, and Ting Wang. CCS, 2021.

• BadNL: Backdoor Attacks against NLP Models with Semantic-preserving Improvements. [pdf] [arXiv-20]

  • Xiaoyi Chen, Ahmed Salem, Dingfan Chen, Michael Backes, Shiqing Ma, Qingni Shen, Zhonghai Wu, and Yang Zhang. ACSAC, 2021.

• Backdoor Attacks on Pre-trained Models by Layerwise Weight Poisoning. [pdf]

  • Linyang Li, Demin Song, Xiaonan Li, Jiehang Zeng, Ruotian Ma, and Xipeng Qiu. EMNLP, 2021.

• T-Miner: A Generative Approach to Defend Against Trojan Attacks on DNN-based Text Classification. [pdf]

  • Ahmadreza Azizi, Ibrahim Asadullah Tahmid, Asim Waheed, Neal Mangaokar, Jiameng Pu, Mobin Javed, Chandan K. Reddy, and Bimal Viswanath. USENIX Security, 2021.

• RAP: Robustness-Aware Perturbations for Defending against Backdoor Attacks on NLP Models. [pdf] [code]

  • Wenkai Yang, Yankai Lin, Peng Li, Jie Zhou, and Xu Sun. EMNLP, 2021.

• ONION: A Simple and Effective Defense Against Textual Backdoor Attacks. [pdf]

  • Fanchao Qi, Yangyi Chen, Mukai Li, Zhiyuan Liu, and Maosong Sun. EMNLP, 2021.

• Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer. [pdf] [code]

  • Fanchao Qi, Yangyi Chen, Xurui Zhang, Mukai Li, Zhiyuan Liu, and Maosong Sun. EMNLP, 2021.

• Rethinking Stealthiness of Backdoor Attack against NLP Models. [pdf] [code]

  • Wenkai Yang, Yankai Lin, Peng Li, Jie Zhou, and Xu Sun. ACL, 2021.

• Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution. [pdf]

  • Fanchao Qi, Yuan Yao, Sophia Xu, Zhiyuan Liu, and Maosong Sun. ACL, 2021.

• Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger. [pdf] [code]

  • Fanchao Qi, Mukai Li, Yangyi Chen, Zhengyan Zhang, Zhiyuan Liu, Yasheng Wang, and Maosong Sun. ACL, 2021.

• Mitigating Data Poisoning in Text Classification with Differential Privacy. [pdf]

  • Chang Xu, Jun Wang, Francisco Guzmán, Benjamin I. P. Rubinstein, and Trevor Cohn. EMNLP-Findings, 2021.

• BFClass: A Backdoor-free Text Classification Framework. [pdf] [code]

  • Zichao Li, Dheeraj Mekala, Chengyu Dong, and Jingbo Shang. EMNLP-Findings, 2021.

• Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models. [pdf] [code]

  • Wenkai Yang, Lei Li, Zhiyuan Zhang, Xuancheng Ren, Xu Sun, and Bin He. NAACL-HLT, 2021.

• Neural Network Surgery: Injecting Data Patterns into Pre-trained Models with Minimal Instance-wise Side Effects. [pdf]

  • Zhiyuan Zhang, Xuancheng Ren, Qi Su, Xu Sun, and Bin He. NAACL-HLT, 2021.

• Text Backdoor Detection Using An Interpretable RNN Abstract Model. [link]

  • Ming Fan, Ziliang Si, Xiaofei Xie, Yang Liu, and Ting Liu. IEEE Transactions on Information Forensics and Security, 2021.

• Textual Backdoor Attack for the Text Classification System. [pdf]

  • Hyun Kwon and Sanghyun Lee. Security and Communication Networks, 2021.

• Weight Poisoning Attacks on Pre-trained Models. [pdf] [code]

  • Keita Kurita, Paul Michel, and Graham Neubig. ACL, 2020.

• Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder. [pdf]

  • Alvin Chan, Yi Tay, Yew-Soon Ong, and Aston Zhang. EMNLP-Findings, 2020.

• A Backdoor Attack Against LSTM-based Text Classification Systems. [pdf]

  • Jiazhu Dai, Chuanshuai Chen, and Yufeng Li. IEEE Access, 2019.

• PerD: Perturbation Sensitivity-based Neural Trojan Detection Framework on NLP Applications. [pdf]

  • Diego Garcia-soto, Huili Chen, and Farinaz Koushanfar. arXiv, 2022.

• Kallima: A Clean-label Framework for Textual Backdoor Attacks. [pdf]

  • Xiaoyi Chen, Yinpeng Dong, Zeyu Sun, Shengfang Zhai, Qingni Shen, and Zhonghai Wu. arXiv, 2022.

• Textual Backdoor Attacks with Iterative Trigger Injection. [pdf] [code]

  • Jun Yan, Vansh Gupta, and Xiang Ren. arXiv, 2022.

• WeDef: Weakly Supervised Backdoor Defense for Text Classification. [pdf]

  • Lesheng Jin, Zihan Wang, and Jingbo Shang. arXiv, 2022.

• Constrained Optimization with Dynamic Bound-scaling for Effective NLPBackdoor Defense. [pdf]

  • Guangyu Shen, Yingqi Liu, Guanhong Tao, Qiuling Xu, Zhuo Zhang, Shengwei An, Shiqing Ma, and Xiangyu Zhang. arXiv, 2022.

• Rethink Stealthy Backdoor Attacks in Natural Language Processing. [pdf]

  • Lingfeng Shen, Haiyun Jiang, Lemao Liu, and Shuming Shi. arXiv, 2022.

• Textual Backdoor Attacks Can Be More Harmful via Two Simple Tricks. [pdf]

  • Yangyi Chen, Fanchao Qi, Zhiyuan Liu, and Maosong Sun. arXiv, 2021.

• Spinning Sequence-to-Sequence Models with Meta-Backdoors. [pdf]

  • Eugene Bagdasaryan and Vitaly Shmatikov. arXiv, 2021.

• Defending against Backdoor Attacks in Natural Language Generation. [pdf] [code]

  • Chun Fan, Xiaoya Li, Yuxian Meng, Xiaofei Sun, Xiang Ao, Fei Wu, Jiwei Li, and Tianwei Zhang. arXiv, 2021.

• Hidden Backdoors in Human-Centric Language Models. [pdf]

  • Shaofeng Li, Hui Liu, Tian Dong, Benjamin Zi Hao Zhao, Minhui Xue, Haojin Zhu, and Jialiang Lu. arXiv, 2021.

• Detecting Universal Trigger’s Adversarial Attack with Honeypot. [pdf]

  • Thai Le, Noseong Park, Dongwon Lee. arXiv, 2020.

• Mitigating Backdoor Attacks in LSTM-based Text Classification Systems by Backdoor Keyword Identification. [pdf]

  • Chuanshuai Chen, and Jiazhu Dai. arXiv, 2020.

• Trojaning Language Models for Fun and Profit. [pdf]

  • Xinyang Zhang, Zheng Zhang, and Ting Wang. arXiv, 2020.

Graph Neural Networks

• Transferable Graph Backdoor Attack. [pdf]

  • Shuiqiao Yang, Bao Gia Doan, Paul Montague, Olivier De Vel, Tamas Abraham, Seyit Camtepe, Damith C. Ranasinghe, and Salil S. Kanhere. RAID, 2022.

• More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks. [pdf]

  • Jing Xu, Rui Wang, Kaitai Liang, and Stjepan Picek. ACSAC, 2022.

• Graph Backdoor. [pdf] [code]

  • Zhaohan Xi, Ren Pang, Shouling Ji, and Ting Wang. USENIX Security, 2021.

• Backdoor Attacks to Graph Neural Networks. [pdf]

  • Zaixi Zhang, Jinyuan Jia, Binghui Wang, and Neil Zhenqiang Gong. SACMAT, 2021.

• Defending Against Backdoor Attack on Graph Nerual Network by Explainability. [pdf]

  • Bingchen Jiang and Zhao Li. arXiv, 2022.

• Link-Backdoor: Backdoor Attack on Link Prediction via Node Injection. [pdf] [code]

  • Haibin Zheng, Haiyang Xiong, Haonan Ma, Guohan Huang, and Jinyin Chen. arXiv, 2022.

• Neighboring Backdoor Attacks on Graph Convolutional Network. [pdf]

  • Liang Chen, Qibiao Peng, Jintang Li, Yang Liu, Jiawei Chen, Yong Li, and Zibin Zheng. arXiv, 2022.

• Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction. [pdf]

  • Jinyin Chen, Haiyang Xiong, Haibin Zheng, Jian Zhang, Guodong Jiang, and Yi Liu. arXiv, 2021.

• Explainability-based Backdoor Attacks Against Graph Neural Networks. [pdf]

  • Jing Xu, Minhui, Xue, and Stjepan Picek. arXiv, 2021.

Point Cloud

• A Backdoor Attack against 3D Point Cloud Classifiers. [pdf] [code]

  • Zhen Xiang, David J. Miller, Siheng Chen, Xi Li, and George Kesidis. ICCV, 2021.

• PointBA: Towards Backdoor Attacks in 3D Point Cloud. [pdf]

  • Xinke Li, Zhiru Chen, Yue Zhao, Zekun Tong, Yabang Zhao, Andrew Lim, and Joey Tianyi Zhou. ICCV, 2021.

• Imperceptible and Robust Backdoor Attack in 3D Point Cloud. [pdf]

  • Kuofeng Gao, Jiawang Bai, Baoyuan Wu, Mengxi Ya, and Shu-Tao Xia. arXiv, 2022.

• Detecting Backdoor Attacks Against Point Cloud Classifiers. [pdf]

  • Zhen Xiang, David J. Miller, Siheng Chen, Xi Li, and George Kesidis. arXiv, 2021.

• Poisoning MorphNet for Clean-Label Backdoor Attack to Point Clouds. [pdf]

  • Guiyu Tian, Wenhao Jiang, Wei Liu, and Yadong Mu. arXiv, 2021.

Acoustics Signal Processing

• Backdoor Attack against Speaker Verification [pdf] [code]

  • Tongqing Zhai, Yiming Li, Ziqi Zhang, Baoyuan Wu, Yong Jiang, and Shu-Tao Xia. ICASSP, 2021.

• DriNet: Dynamic Backdoor Attack against Automatic Speech Recognization Models. [link]

  • Jianbin Ye, Xiaoyuan Liu, Zheng You, Guowei Li, and Bo Liu. Applied Sciences, 2022.

• Can You Hear It? Backdoor Attacks via Ultrasonic Triggers. [pdf] [code]

  • Stefanos Koffas, Jing Xu, Mauro Conti, and Stjepan Picek. WiseML, 2022.

• Going in Style: Audio Backdoors Through Stylistic Transformations. [pdf]

  • Stefanos Koffas, Luca Pajola, Stjepan Picek, and Mauro Conti. arXiv, 2022.

Medical Science

• FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis. [pdf]

  • Yu Feng, Benteng Ma, Jing Zhang, Shanshan Zhao, Yong Xia, and Dacheng Tao. CVPR, 2022.

• Exploiting Missing Value Patterns for a Backdoor Attack on Machine Learning Models of Electronic Health Records: Development and Validation Study. [link]

  • Byunggill Joe, Yonghyeon Park, Jihun Hamm, Insik Shin, and Jiyeon Lee. JMIR Medical Informatics, 2022.

• Machine Learning with Electronic Health Records is vulnerable to Backdoor Trigger Attacks. [pdf]

  • Byunggill Joe, Akshay Mehra, Insik Shin, and Jihun Hamm. AAAI Workshop, 2021.

• Explainability Matters: Backdoor Attacks on Medical Imaging. [pdf]

  • Munachiso Nwadike, Takumi Miyawaki, Esha Sarkar, Michail Maniatakos, and Farah Shamout. AAAI Workshop, 2021.

• TRAPDOOR: Repurposing Backdoors to Detect Dataset Bias in Machine Learning-based Genomic Analysis. [pdf]

  • Esha Sarkar and Michail Maniatakos. arXiv, 2021.

Cybersecurity

• VulnerGAN: A Backdoor Attack through Vulnerability Amplification against Machine Learning-based Network Intrusion Detection Systems. [link] [code]

  • Guangrui Liu, Weizhe Zhang, Xinjie Li, Kaisheng Fan, and Shui Yu. Information Sciences, 2022.

• Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers. [pdf]

  • Giorgio Severi, Jim Meyer, Scott Coull, and Alina Oprea. USENIX Security, 2021.

• Backdoor Attack on Machine Learning Based Android Malware Detectors. [link]

  • Chaoran Li, Xiao Chen, Derui Wang, Sheng Wen, Muhammad Ejaz Ahmed, Seyit Camtepe, and Yang Xiang. IEEE Transactions on Dependable and Secure Computing, 2021.

• Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers. [pdf]

  • Limin Yang, Zhi Chen, Jacopo Cortellazzi, Feargus Pendlebury, Kevin Tu, Fabio Pierazzi, Lorenzo Cavallaro, and Gang Wang. arXiv, 2022.

Others

• Few-Shot Backdoor Attacks on Visual Object Tracking. [pdf] [code]

  • Yiming Li, Haoxiang Zhong, Xingjun Ma, Yong Jiang, and Shu-Tao Xia. ICLR, 2022.

• Backdoor Attacks on Crowd Counting. [pdf]

  • Yuhua Sun, Tailai Zhang, Xingjun Ma, Pan Zhou, Jian Lou, Zichuan Xu, Xing Di, Yu Cheng, and Lichao Sun. ACM MM, 2022.

• Backdoor Attacks on the DNN Interpretation System. [pdf]

  • Shihong Fang and Anna Choromanska. AAAI, 2022.

• The Devil is in the GAN: Defending Deep Generative Models Against Backdoor Attacks. [pdf] [code] [demo]

  • Ambrish Rawat, Killian Levacher, and Mathieu Sinn. ESORICS, 2022.

• Object-Oriented Backdoor Attack Against Image Captioning. [link]

  • Meiling Li, Nan Zhong, Xinpeng Zhang, Zhenxing Qian, and Sheng Li. ICASSP, 2022.

• When Does Backdoor Attack Succeed in Image Reconstruction? A Study of Heuristics vs. Bi-Level Solution. [link]

  • Vardaan Taneja, Pin-Yu Chen, Yuguang Yao, and Sijia Liu. ICASSP, 2022.

• An Interpretive Perspective: Adversarial Trojaning Attack on Neural-Architecture-Search Enabled Edge AI Systems. [link]

  • Ship Peng Xu, Ke Wang, Md. Rafiul Hassan, Mohammad Mehedi Hassan, and Chien-Ming Chen. IEEE Transactions on Industrial Informatics, 2022.

• A Triggerless Backdoor Attack and Defense Mechanism for Intelligent Task Offloading in Multi-UAV Systems. [link]

  • Shafkat Islam, Shahriar Badsha, Ibrahim Khalil, Mohammed Atiquzzaman, and Charalambos Konstantinou. IEEE Internet of Things Journal, 2022.

• Multi-Target Invisibly Trojaned Networks for Visual Recognition and Detection. [pdf]

  • Xinzhe Zhou, Wenhao Jiang, Sheng Qi, and Yadong Mu. IJCAI, 2021.

• Hidden Backdoor Attack against Semantic Segmentation Models. [pdf]

  • Yiming Li, Yanjie Li, Yalei Lv, Yong Jiang, and Shu-Tao Xia. ICLR Workshop, 2021.

• Adversarial Targeted Forgetting in Regularization and Generative Based Continual Learning Models. [link]

  • Muhammad Umer and Robi Polikar. IJCNN, 2021.

• Targeted Forgetting and False Memory Formation in Continual Learners through Adversarial Backdoor Attacks. [pdf]

  • Muhammad Umer, Glenn Dawson, Robi Polikar. IJCNN, 2020.

• Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learning. [pdf]

  • Kemal Davaslioglu, and Yalin E. Sagduyu. DySPAN, 2019.

• BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean Label. [pdf]

  • Shengshan Hu, Ziqi Zhou, Yechao Zhang, Leo Yu Zhang, Yifeng Zheng, Yuanyuan HE, and Hai Jin. arXiv, 2022.

• A Temporal Chrominance Trigger for Clean-label Backdoor Attack against Anti-spoof Rebroadcast Detection. [pdf]

  • Wei Guo, Benedetta Tondi, and Mauro Barni. arXiv, 2022.

• MACAB: Model-Agnostic Clean-Annotation Backdoor to Object Detection with Natural Trigger in Real-World. [pdf]

  • Hua Ma, Yinshan Li, Yansong Gao, Zhi Zhang, Alsharif Abuadbba, Anmin Fu, Said F. Al-Sarawi, Nepal Surya, and Derek Abbott. arXiv, 2022.

• BadDet: Backdoor Attacks on Object Detection. [pdf]

  • Shih-Han Chan, Yinpeng Dong, Jun Zhu, Xiaolu Zhang, and Jun Zhou. arXiv, 2022.

• Backdoor Attacks on Bayesian Neural Networks using Reverse Distribution. [pdf]

  • Zhixin Pan and Prabhat Mishra. arXiv, 2022.

• Backdooring Explainable Machine Learning. [pdf]

  • Maximilian Noppel, Lukas Peter, and Christian Wressnegger. arXiv, 2022.

• Clean-Annotation Backdoor Attack against Lane Detection Systems in the Wild. [pdf]

  • Xingshuo Han, Guowen Xu, Yuan Zhou, Xuehuan Yang, Jiwei Li, and Tianwei Zhang. arXiv, 2022.

• Dangerous Cloaking: Natural Trigger based Backdoor Attacks on Object Detectors in the Physical World. [pdf]

  • Hua Ma, Yinshan Li, Yansong Gao, Alsharif Abuadbba, Zhi Zhang, Anmin Fu, Hyoungshick Kim, Said F. Al-Sarawi, Nepal Surya, and Derek Abbott. arXiv, 2022.

• Targeted Trojan-Horse Attacks on Language-based Image Retrieval. [pdf]

  • Fan Hu, Aozhu Chen, and Xirong Li. arXiv, 2022.

• Is Multi-Modal Necessarily Better? Robustness Evaluation of Multi-modal Fake News Detection. [pdf]

  • Jinyin Chen, Chengyu Jia, Haibin Zheng, Ruoxi Chen, and Chenbo Fu. arXiv, 2022.

• Dual-Key Multimodal Backdoors for Visual Question Answering. [pdf]

  • Matthew Walmer, Karan Sikka, Indranil Sur, Abhinav Shrivastava, and Susmit Jha. arXiv, 2021.

• Clean-label Backdoor Attack against Deep Hashing based Retrieval. [pdf]

  • Kuofeng Gao, Jiawang Bai, Bin Chen, Dongxian Wu, and Shu-Tao Xia. arXiv, 2021.

• Backdoor Attacks on Network Certification via Data Poisoning. [pdf]

  • Tobias Lorenz, Marta Kwiatkowska, and Mario Fritz. arXiv, 2021.

• Backdoor Attack and Defense for Deep Regression. [pdf]

  • Xi Li, George Kesidis, David J. Miller, and Vladimir Lucic. arXiv, 2021.

• The Devil is in the GAN: Defending Deep Generative Models Against Backdoor Attacks. [pdf]

  • Ambrish Rawat, Killian Levacher, and Mathieu Sinn. arXiv, 2021.

• BAAAN: Backdoor Attacks Against Autoencoder and GAN-Based Machine Learning Models. [pdf]

  • Ahmed Salem, Yannick Sautter, Michael Backes, Mathias Humbert, and Yang Zhang. arXiv, 2020.

• DeepObliviate: A Powerful Charm for Erasing Data Residual Memory in Deep Neural Networks. [pdf]

  • Yingzhe He, Guozhu Meng, Kai Chen, Jinwen He, and Xingbo Hu. arXiv, 2021.

• Backdoors in Neural Models of Source Code. [pdf]

  • Goutham Ramakrishnan, and Aws Albarghouthi. arXiv, 2020.

• EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks. [pdf]

  • Lubin Meng, Jian Huang, Zhigang Zeng, Xue Jiang, Shan Yu, Tzyy-Ping Jung, Chin-Teng Lin, Ricardo Chavarriaga, and Dongrui Wu. arXiv, 2020.

• Bias Busters: Robustifying DL-based Lithographic Hotspot Detectors Against Backdooring Attacks. [pdf]

  • Kang Liu, Benjamin Tan, Gaurav Rajavendra Reddy, Siddharth Garg, Yiorgos Makris, and Ramesh Karri. arXiv, 2020.

Evaluation and Discussion

• TROJANZOO: Everything You Ever Wanted to Know about Neural Backdoors (But were Afraid to Ask). [pdf] [code]

  • Ren Pang, Zheng Zhang, Xiangshan Gao, Zhaohan Xi, Shouling Ji, Peng Cheng, and Ting Wang. EuroS&P, 2022.

• BackdoorBench: A Comprehensive Benchmark of Backdoor Learning. [pdf] [code] [website]

  • Baoyuan Wu, Hongrui Chen, Mingda Zhang, Zihao Zhu, Shaokui Wei, Danni Yuan, Chao Shen, and Hongyuan Zha. NeurIPS, 2022.

• A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks. [pdf] [code]

  • Ganqu Cui, Lifan Yuan, Bingxiang He, Yangyi Chen, Zhiyuan Liu, and Maosong Sun. NeurIPS, 2022.

• Backdoor Defense via Decoupling the Training Process. [pdf] [code]

  • Kunzhe Huang, Yiming Li, Baoyuan Wu, Zhan Qin, and Kui Ren. ICLR, 2022.

• How to Inject Backdoors with Better Consistency: Logit Anchoring on Clean Data. [pdf]

  • Zhiyuan Zhang, Lingjuan Lyu, Weiqiang Wang, Lichao Sun, and Xu Sun. ICLR, 2022.

• Defending against Model Stealing via Verifying Embedded External Features. [pdf] [code]

  • Yiming Li, Linghui Zhu, Xiaojun Jia, Yong Jiang, Shu-Tao Xia, and Xiaochun Cao. AAAI, 2022. (Discuss the limitations of using backdoor attacks for model watermarking)

• Susceptibility & Defense of Satellite Image-trained Convolutional Networks to Backdoor Attacks. [link]

  • Ethan Brewer, Jason Lin, and Dan Runfola. Information Sciences, 2021.

• Data-Efficient Backdoor Attacks. [pdf] [code]

  • Pengfei Xia, Ziqiang Li, Wei Zhang, and Bin Li. IJCAI, 2022.

• Excess Capacity and Backdoor Poisoning. [pdf]

  • Naren Sarayu Manoj and Avrim Blum. NeurIPS, 2021.

• Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks. [pdf] [code]

  • Avi Schwarzschild, Micah Goldblum, Arjun Gupta, John P Dickerson, and Tom Goldstein. ICML, 2021.

• Rethinking the Backdoor Attacks' Triggers: A Frequency Perspective. [pdf]

  • Yi Zeng, Won Park, Z. Morley Mao, and Ruoxi Jia. ICCV, 2021.

• Backdoor Attacks Against Deep Learning Systems in the Physical World. [pdf] [Master Thesis]

  • Emily Wenger, Josephine Passanati, Yuanshun Yao, Haitao Zheng, and Ben Y. Zhao. CVPR, 2021.

• Can Optical Trojans Assist Adversarial Perturbations? [pdf]

  • Adith Boloor, Tong Wu, Patrick Naughton, Ayan Chakrabarti, Xuan Zhang, and Yevgeniy Vorobeychik. ICCV Workshop, 2021.

• On the Trade-off between Adversarial and Backdoor Robustness. [pdf]

  • Cheng-Hsin Weng, Yan-Ting Lee, and Shan-Hung Wu. NeurIPS, 2020.

• A Tale of Evil Twins: Adversarial Inputs versus Poisoned Models. [pdf] [code]

  • Ren Pang, Hua Shen, Xinyang Zhang, Shouling Ji, Yevgeniy Vorobeychik, Xiapu Luo, Alex Liu, and Ting Wang. CCS, 2020.

• Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers. [pdf]

  • Loc Truong, Chace Jones, Brian Hutchinson, Andrew August, Brenda Praggastis, Robert Jasper, Nicole Nichols, and Aaron Tuor. CVPR Workshop, 2020.

• On Evaluating Neural Network Backdoor Defenses. [pdf]

  • Akshaj Veldanda, and Siddharth Garg. NeurIPS Workshop, 2020.

• Attention Hijacking in Trojan Transformers. [pdf]

  • Weimin Lyu, Songzhu Zheng, Tengfei Ma, Haibin Ling, and Chao Chen. arXiv, 2022.

• Game of Trojans: A Submodular Byzantine Approach. [pdf]

  • Dinuka Sahabandu, Arezoo Rajabi, Luyao Niu, Bo Li, Bhaskar Ramasubramanian, and Radha Poovendran. arXiv, 2022.

• Auditing Visualizations: Transparency Methods Struggle to Detect Anomalous Behavior. [pdf] [code]

  • Jean-Stanislas Denain and Jacob Steinhardt. arXiv, 2022.

• Natural Backdoor Datasets. [pdf]

  • Emily Wenger, Roma Bhattacharjee, Arjun Nitin Bhagoji, Josephine Passananti, Emilio Andere, Haitao Zheng, and Ben Y. Zhao. arXiv, 2022.

• A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks. [pdf] [code]

  • Ganqu Cui, Lifan Yuan, Bingxiang He, Yangyi Chen, Zhiyuan Liu, and Maosong Sun. arXiv, 2022.

• Can Backdoor Attacks Survive Time-Varying Models? [pdf]

  • Huiying Li, Arjun Nitin Bhagoji, Ben Y. Zhao, and Haitao Zheng. arXiv, 2022.

• Dynamic Backdoor Attacks with Global Average Pooling [pdf] [code]

  • Stefanos Koffas, Stjepan Picek, and Mauro Conti. arXiv, 2022.

• Planting Undetectable Backdoors in Machine Learning Models. [pdf]

  • Shafi Goldwasser, Michael P. Kim, Vinod Vaikuntanathan, and Or Zamir. arXiv, 2022.

• Towards A Critical Evaluation of Robustness for Deep Learning Backdoor Countermeasures. [pdf]

  • Huming Qiu, Hua Ma, Zhi Zhang, Alsharif Abuadbba, Wei Kang, Anmin Fu, and Yansong Gao. arXiv, 2022.

• Neural Network Trojans Analysis and Mitigation from the Input Domain. [pdf]

  • Zhenting Wang, Hailun Ding, Juan Zhai, and Shiqing Ma. arXiv, 2022.

• Widen The Backdoor To Let More Attackers In. [pdf]

  • Siddhartha Datta, Giulio Lovisotto, Ivan Martinovic, and Nigel Shadbolt. arXiv, 2021.

• Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions. [pdf]

  • Antonio Emanuele Cinà, Kathrin Grosse, Sebastiano Vascon, Ambra Demontis, Battista Biggio, Fabio Roli, and Marcello Pelillo. arXiv, 2021.

• Rethinking the Trigger of Backdoor Attack. [pdf]

  • Yiming Li, Tongqing Zhai, Baoyuan Wu, Yong Jiang, Zhifeng Li, and Shutao Xia. arXiv, 2020.

• Poisoned Classifiers are Not Only Backdoored, They are Fundamentally Broken. [pdf] [code]

  • Mingjie Sun, Siddhant Agarwal, and J. Zico Kolter. ICLR Workshop, 2021.

• Effect of Backdoor Attacks over the Complexity of the Latent Space Distribution. [pdf] [code]

  • Henry D. Chacon, and Paul Rad. arXiv, 2020.

• Trembling Triggers: Exploring the Sensitivity of Backdoors in DNN-based Face Recognition. [pdf]

  • Cecilia Pasquini, and Rainer Böhme. EURASIP Journal on Information Security, 2020.

• Noise-response Analysis for Rapid Detection of Backdoors in Deep Neural Networks. [pdf]

  • N. Benjamin Erichson, Dane Taylor, Qixuan Wu, and Michael W. Mahoney. arXiv, 2020.

Backdoor Attack for Positive Purposes

• Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection. [pdf] [code]

  • Yiming Li, Yang Bai, Yong Jiang, Yong Yang, Shu-Tao Xia, and Bo Li. NeurIPS, 2022.

• Membership Inference via Backdooring. [pdf] [code]

  • Hongsheng Hu, Zoran Salcic, Gillian Dobbie, Jinjun Chen, Lichao Sun, and Xuyun Zhang. IJCAI, 2022.

• Neural Network Surgery: Injecting Data Patterns into Pre-trained Models with Minimal Instance-wise Side Effects. [pdf]

  • Zhiyuan Zhang, Xuancheng Ren, Qi Su, Xu Sun and Bin He. NAACL-HLT, 2021.

• One Step Further: Evaluating Interpreters using Metamorphic Testing. [pdf]

  • Ming Fan, Jiali Wei, Wuxia Jin, Zhou Xu, Wenying Wei, and Ting Liu. ISSTA, 2022.

• What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors. [pdf]

  • Yi-Shan Lin, Wen-Chuan Lee, and Z. Berkay Celik. KDD, 2021.

• Using Honeypots to Catch Adversarial Attacks on Neural Networks. [pdf]

  • Shawn Shan, Emily Wenger, Bolun Wang, Bo Li, Haitao Zheng, Ben Y. Zhao. CCS, 2020. (Note: Unfortunately, it was bypassed by Nicholas Carlini most recently. [arXiv])

• Turning Your Weakness into a Strength: Watermarking Deep Neural Networks by Backdooring. [pdf] [code]

  • Yossi Adi, Carsten Baum, Moustapha Cisse, Benny Pinkas, and Joseph Keshet. USENIX Security, 2018.

• Open-sourced Dataset Protection via Backdoor Watermarking. [pdf] [code]

  • Yiming Li, Ziqi Zhang, Jiawang Bai, Baoyuan Wu, Yong Jiang, and Shu-Tao Xia. NeurIPS Workshop, 2020.

• Protecting Deep Cerebrospinal Fluid Cell Image Processing Models with Backdoor and Semi-Distillation. [link]

  • FangQi Li, Shilin Wang, and Zhenhai Wang. DICTA, 2021.

• Debiasing Backdoor Attack: A Benign Application of Backdoor Attack in Eliminating Data Bias. [pdf]

  • Shangxi Wu, Qiuyang He, Yi Zhang, and Jitao Sang. arXiv, 2022.

• Watermarking Graph Neural Networks based on Backdoor Attacks. [pdf]

  • Jing Xu and Stjepan Picek. arXiv, 2021.

• CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning. [pdf]

  • Zhensu Sun, Xiaoning Du, Fu Song, Mingze Ni, and Li Li. arXiv, 2021.

• What Do Deep Nets Learn? Class-wise Patterns Revealed in the Input Space. [pdf]

  • Shihao Zhao, Xingjun Ma, Yisen Wang, James Bailey, Bo Li, and Yu-Gang Jiang. arXiv, 2021.

• A Stealthy and Robust Fingerprinting Scheme for Generative Models. [pdf]

  • Guanlin Li, Shangwei Guo, Run Wang, Guowen Xu, and Tianwei Zhang. arXiv, 2021.

• Towards Probabilistic Verification of Machine Unlearning. [pdf] [code]

  • David Marco Sommer, Liwei Song, Sameer Wagh, and Prateek Mittal. arXiv, 2020.

Competition

• Trojan Detection Challenge
• IARPA TrojAI Competition