Backdoor Learning Resources

This Github repository summarizes a curated list of Backdoor Learning resources. For more details and the categorization criteria, please refer to our survey.

Why Backdoor Learning?

Backdoor learning is an emerging research area, which discusses the security issues of the training process towards machine learning algorithms. It is critical for safely adopting third-party algorithms in reality. Although backdoor learning shares certain similarity with adversarial learning (which concentrates on the security issues of the inference process), they do have essential differences and can be easily distinguished.

Note: 'Backdoor' is also commonly called the 'Neural Trojan' or 'Trojan'.

Citation

If our repo or survey is useful for your research, please cite our paper as follows:

@article{li2020backdoor,
  title={Backdoor learning: A survey},
  author={Li, Yiming and Wu, Baoyuan and Jiang, Yong and Li, Zhifeng and Xia, Shu-Tao},
  journal={arXiv preprint arXiv:2007.08745},
  year={2020}
}

Contributing

Please help to contribute this list by contacting me or add pull request

Markdown format:

markdown
- Paper Name. 
  [[pdf]](link) 
  [[code]](link)
  - Author 1, Author 2, and Author 3. *Conference/Journal*, Year.

Table of Contents

• Survey
• Dissertation and Thesis
• Image and Video Classification
  • Poisoning-based Attack
  • Non-poisoning-based Attack
  • Backdoor Defense
  • Preprocessing based Empirical Defense
  • Model Reconstruction based Empirical Defense
  • Trigger Synthesis based Empirical Defense
  • Model Diagnosis based Empirical Defense
  • Poison Suppression based Empirical Defense
  • Sample Filtering based Empirical Defense
  • Certificated Defense
• Attack and Defense Towards Other Paradigms and Tasks
  • Collaborative Learning
  • Transfer Learning
  • Reinforcement Learning
  • Semi-Supervised and Self-Supervised Learning
  • Natural Language Processing
  • Graph Neural Networks
  • Point Cloud
  • Acoustics Signal Processing
  • Medical Science
  • Others
• Discussion and Evaluation
• Backdoor Attack for Positive Purposes
• Toolbox
• Competition

Survey

• Backdoor Learning: A Survey. [pdf]

  • Yiming Li, Baoyuan Wu, Yong Jiang, Zhifeng Li, and Shu-Tao Xia. 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. arXiv, 2020.

• 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.

• Deep Learning Backdoors. [pdf]

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

• A Survey on Neural Trojans. [pdf]

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

Dissertation and Thesis

• 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.

• 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 Wenger. Master Thesis at University of Chicago, 2020.

Image and Video Classification

Poisoning-based Attack

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.

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

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

• 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.

• 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.

• Simtrojan: Stealthy Backdoor Attack. [link]

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

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

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

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

  • Jie zhang, Dongdong Chen, Jing Liao, Qidong Huang, Gang Hua, Weiming Zhang, and Nenghai Yu. 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

• 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.

• 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.

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

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

• 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.

• Class-Oriented Poisoning Attack. [pdf]

  • Bingyin Zhao, and Yingjie Lao. arXiv, 2020.

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

  • Ahmed Salem, Rui Wen, Michael Backes, Shiqing Ma, and Yang Zhang. arXiv, 2020.
    

2019

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

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

• 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.

• 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.

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

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

• 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.

• Subnet Replacement: Deployment-stage Backdoor Attack against Deep Neural Networks in Gray-box Setting. [pdf]

  • Xiangyu Qi, Jifeng Zhu, Chulin Xie, and Yong Yang. ICLR Workshop, 2021.

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

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

• Handcrafted Backdoors in Deep Neural Networks. [pdf]

  • Sanghyun Hong, Nicholas Carlini, and Alexey Kurakin. arXiv, 2021.

• Manipulating SGD with Data Ordering Attacks. [pdf]

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

• Stealthy Backdoors as Compression Artifacts. [pdf]

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

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

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

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

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

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

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

Backdoor Defense

Preprocessing based Empirical Defense

• Rethinking the Trigger of Backdoor Attack. [pdf]

  • Yiming Li, Tongqing Zhai, Baoyuan Wu, Yong Jiang, Zhifeng Li, and Shutao Xia. arXiv, 2020.

• 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.

• 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

• 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.

• 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.

• 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.

• 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.

• 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

• 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.

• 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.

• 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.

• 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

• 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.

• 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.

• Trojan Signatures in DNN Weights. [pdf]

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

• Topological Detection of Trojaned Neural Networks. [pdf]

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

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

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

• 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

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

  • 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.
    

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

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

• A Unified Framework for Analyzing and Detecting Malicious Examples of DNN Models. [pdf]

  • Kaidi Jin, Tianwei Zhang, Chao Shen, Yufei Chen, Ming Fan, Chenhao Lin, and Ting Liu. 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

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

  • Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar, 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.

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

  • Maurice Weber, Xiaojun Xu, Bojan Karlas, Ce Zhang, and Bo Li. arXiv, 2020.

Attack and Defense Towards Other Paradigms and Tasks

Collaborative Learning

• 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.

• 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.

• How to Backdoor Federated Learning. [pdf]

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

• 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.

• 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.

• 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.

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

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

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

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

• 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.

• 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.

• 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.

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

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

• 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.

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

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

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

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

• 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

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

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

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

  • 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.

• 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. arXiv, 2020.

• 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

• 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.

• 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.

• 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.

• Poisoning and Backdooring Contrastive Learning. [pdf]

  • Nicholas Carlini and Andreas Terzis. arXiv, 2021.

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

  • Aniruddha Saha, Ajinkya Tejankar, Soroush Abbasi Koohpayegani, and Hamed Pirsiavash. arXiv, 2021.

Natural Language Processing

• 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.

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

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

• 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.

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

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

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

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

• 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.

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

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

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

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

• 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. 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.
    

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

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

• BadNL: Backdoor Attacks Against NLP Models. [pdf]

  • Xiaoyi Chen, Ahmed Salem, Michael Backes, Shiqing Ma, and Yang Zhang. arXiv, 2020.

Graph Neural Networks

• 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. NeurIPS Workshop, 2020.
    

• 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.

• 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.

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

  • Stefanos Koffas, Jing Xu, Mauro Conti, and Stjepan Picek. arXiv, 2021.

Medical Science

• 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.

Others

• 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.

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

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

• 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.

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

  • Shihong Fang, and Anna Choromanska. NeurIPS Workshop, 2020.

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

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

• 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.

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

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

• 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.

Discussion and Evaluation

• 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.

• 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.

• Excess Capacity and Backdoor Poisoning. [pdf]

  • Naren Sarayu Manoj and Avrim Blum. arXiv, 2021.

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

  • Zhiyuan Zhang, Lingjuan Lyu, Weiqiang Wang, Lichao Sun, and Xu Sun. arXiv, 2021.

• 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.
    

• 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. 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

• Open-sourced Dataset Protection via Backdoor Watermarking. [pdf]

  • Yiming Li, Ziqi Zhang, Jiawang Bai, Baoyuan Wu, Yong Jiang, and Shu-Tao Xia. NeurIPS Workshop, 2020.

• 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.

• 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.

• 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. arXiv, 2020.

• Towards Probabilistic Verification of Machine Unlearning. [pdf] [code]

  • David Marco Sommer, Liwei Song, Sameer Wagh, and Prateek Mittal. arXiv, 2020.

Toolbox

• TrojanZoo
• backdoors101

Competition

• IARPA TrojAI Competition