This Github repository summarizes a list of Backdoor Learning resources. For more details and the categorization criteria, please refer to our survey.
We will try our best to continuously maintain this Github Repository in a daily or weekly manner.
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 training resources or models in reality.
Note: 'Backdoor' is also commonly called the 'Neural Trojan' or 'Trojan'.
If our repo or survey is useful for your research, please cite our paper as follows:
@article{li2022backdoor,
title={Backdoor learning: A survey},
author={Li, Yiming and Jiang, Yong and Li, Zhifeng and Xia, Shu-Tao},
journal={IEEE Transactions on Neural Networks and Learning Systems},
year={2022}
}
Please help to contribute this list by contacting me or add pull request
Markdown format:
- Paper Name.
[[pdf]](link)
[[code]](link)
- Author 1, Author 2, and Author 3. *Conference/Journal*, Year.
- Survey
- Toolbox
- Dissertation and Thesis
- Image and Video Classification
- Attack and Defense Towards Other Paradigms and Tasks
- Evaluation and Discussion
- Backdoor Attack for Positive Purposes
- Competition
-
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.
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Deep Learning Backdoors. [pdf]
- Shaofeng Li, Shiqing Ma, Minhui Xue, and Benjamin Zi Hao Zhao. arXiv, 2020.
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Toward Robust and Communication Efficient Distributed Machine Learning. [pdf]
- Hongyi Wang. Ph.D. Dissertation at University of Wisconsin–Madison, 2021.
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Towards Robust Image Classification with Deep Learning and Real-Time DNN Inference on Mobile. [pdf]
- Pu Zhao. Ph.D. Dissertation at Northeastern University, 2021.
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Countermeasures Against Backdoor, Data Poisoning, and Adversarial Attacks. [pdf]
- Henry Daniel. Ph.D. Dissertation at University of Texas at San Antonio, 2021.
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Understanding and Mitigating the Impact of Backdooring Attacks on Deep Neural Networks. [pdf]
- Kang Liu. Ph.D. Dissertation at New York University, 2021.
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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.
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Backdoor Defenses. [pdf]
- Andrea Milakovic. Master Thesis at Technischen Universität Wien, 2021.
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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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
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Backdoor Attacks on Vision Transformers. [pdf] [code]
- Akshayvarun Subramanya, Aniruddha Saha, Soroush Abbasi Koohpayegani, Ajinkya Tejankar, and Hamed Pirsiavash. arXiv, 2022.
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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.
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Circumventing Backdoor Defenses That Are Based on Latent Separability. [pdf] [code]
- Xiangyu Qi, Tinghao Xie, Saeed Mahloujifar, and Prateek Mittal. arXiv, 2022.
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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.
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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.
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Trojan Horse Training for Breaking Defenses against Backdoor Attacks in Deep Learning. [pdf]
- Arezoo Rajabi, Bhaskar Ramasubramanian, and Radha Poovendran. arXiv, 2022.
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Label-Smoothed Backdoor Attack. [pdf]
- Minlong Peng, Zidi Xiong, Mingming Sun, and Ping Li. arXiv, 2022.
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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.
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Compression-Resistant Backdoor Attack against Deep Neural Networks. [pdf]
- Mingfu Xue, Xin Wang, Shichang Sun, Yushu Zhang, Jian Wang, and Weiqiang Liu. arXiv, 2022.
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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.
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LIRA: Learnable, Imperceptible and Robust Backdoor Attacks. [pdf]
- Khoa Doan, Yingjie Lao, Weijie Zhao, and Ping Li. ICCV, 2021.
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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.
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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.
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Deep Feature Space Trojan Attack of Neural Networks by Controlled Detoxification. [pdf] [code]
- Siyuan Cheng, Yingqi Liu, Shiqing Ma, and Xiangyu Zhang. AAAI, 2021.
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WaNet - Imperceptible Warping-based Backdoor Attack. [pdf] [code]
- Tuan Anh Nguyen, and Anh Tuan Tran. ICLR, 2021.
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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.
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Invisible Poison: A Blackbox Clean Label Backdoor Attack to Deep Neural Networks. [pdf]
- Rui Ning, Jiang Li, ChunSheng Xin, and Hongyi Wu. INFOCOM, 2021.
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Backdoor Attack in the Physical World. [pdf] [extension]
- Yiming Li, Tongqing Zhai, Yong Jiang, Zhifeng Li, and Shu-Tao Xia. ICLR Workshop, 2021.
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A Master Key Backdoor for Universal Impersonation Attack against DNN-based Face Verification. [link]
- WeiGuo, Benedetta Tondi, and Mauro Barni. Pattern Recognition Letters, 2021.
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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.
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Use Procedural Noise to Achieve Backdoor Attack. [link] [code]
- Xuan Chen, Yuena Ma, and Shiwei Lu. IEEE Access, 2021.
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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.
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Simtrojan: Stealthy Backdoor Attack. [link]
- Yankun Ren, Longfei Li, and Jun Zhou. ICIP, 2021.
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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.
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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.
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Check Your Other Door! Establishing Backdoor Attacks in the Frequency Domain. [pdf]
- Hasan Abed Al Kader Hammoud and Bernard Ghanem. arXiv, 2021.
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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.
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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.
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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.
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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.
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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.
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Composite Backdoor Attack for Deep Neural Network by Mixing Existing Benign Features. [pdf]
- Junyu Lin, Lei Xu, Yingqi Liu, Xiangyu Zhang. CCS, 2020.
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Input-Aware Dynamic Backdoor Attack. [pdf] [code]
- Anh Nguyen, and Anh Tran. NeurIPS, 2020.
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Bypassing Backdoor Detection Algorithms in Deep Learning. [pdf]
- Te Juin Lester Tan, and Reza Shokri. EuroS&P, 2020.
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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.
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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.
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Escaping Backdoor Attack Detection of Deep Learning. [link]
- Yayuan Xiong, Fengyuan Xu, Sheng Zhong, and Qun Li. IFIP SEC, 2020.
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Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks. [pdf] [code]
- Yunfei Liu, Xingjun Ma, James Bailey, and Feng Lu. ECCV, 2020.
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Live Trojan Attacks on Deep Neural Networks. [pdf] [code]
- Robby Costales, Chengzhi Mao, Raphael Norwitz, Bryan Kim, and Junfeng Yang. CVPR Workshop, 2020.
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Backdooring and Poisoning Neural Networks with Image-Scaling Attacks. [pdf]
- Erwin Quiring, and Konrad Rieck. IEEE S&P Workshop, 2020.
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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.
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FaceHack: Triggering Backdoored Facial Recognition Systems Using Facial Characteristics. [pdf]
- Esha Sarkar, Hadjer Benkraouda, and Michail Maniatakos. arXiv, 2020.
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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.
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A New Backdoor Attack in CNNS by Training Set Corruption Without Label Poisoning. [pdf]
- M.Barni, K.Kallas, and B.Tondi. ICIP, 2019.
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Label-Consistent Backdoor Attacks. [pdf] [code]
- Alexander Turner, Dimitris Tsipras, and Aleksander Madry. arXiv, 2019.
- Trojaning Attack on Neural Networks.
[pdf]
[code]
- Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, and Juan Zhai. NDSS, 2018.
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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).
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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.
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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.
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ProFlip: Targeted Trojan Attack with Progressive Bit Flips. [pdf]
- Huili Chen, Cheng Fu, Jishen Zhao, and Farinaz Koushanfar. ICCV, 2021.
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TBT: Targeted Neural Network Attack with Bit Trojan. [pdf] [code]
- Adnan Siraj Rakin, Zhezhi He, and Deliang Fan. CVPR, 2020.
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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.
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Can Adversarial Weight Perturbations Inject Neural Backdoors? [pdf]
- Siddhant Garg, Adarsh Kumar, Vibhor Goel, and Yingyu Liang. CIKM, 2020.
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TrojViT: Trojan Insertion in Vision Transformers. [pdf]
- Mengxin Zheng, Qian Lou, and Lei Jiang. arXiv, 2022.
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Versatile Weight Attack via Flipping Limited Bits. [pdf]
- Jiawang Bai, Baoyuan Wu, Zhifeng Li, and Shu-Tao Xia. arXiv, 2022.
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Toward Realistic Backdoor Injection Attacks on DNNs using Rowhammer. [pdf]
- M. Caner Tol, Saad Islam, Berk Sunar, and Ziming Zhang. 2022.
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TrojanNet: Embedding Hidden Trojan Horse Models in Neural Network. [pdf]
- Chuan Guo, Ruihan Wu, and Kilian Q. Weinberger. arXiv, 2020.
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Backdooring Convolutional Neural Networks via Targeted Weight Perturbations. [pdf]
- Jacob Dumford, and Walter Scheirer. arXiv, 2018.
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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.
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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.
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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.
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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.
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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.
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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.
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Architectural Backdoors in Neural Networks. [pdf]
- Mikel Bober-Irizar, Ilia Shumailov, Yiren Zhao, Robert Mullins, and Nicolas Papernot. arXiv, 2022.
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Planting Undetectable Backdoors in Machine Learning Models. [pdf]
- Shafi Goldwasser, Michael P. Kim, Vinod Vaikuntanathan, and Or Zamir. 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 Attack in the Physical World. [pdf] [extension]
- Yiming Li, Tongqing Zhai, Yong Jiang, Zhifeng Li, and Shu-Tao Xia. ICLR Workshop, 2021.
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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.
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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.
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Defending Deep Neural Networks against Backdoor Attack by Using De-trigger Autoencoder. [pdf]
- Hyun Kwon. IEEE Access, 2021.
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Defending Backdoor Attacks on Vision Transformer via Patch Processing. [pdf]
- Khoa D. Doan, Yingjie Lao, Peng Yang, and Ping Li. arXiv, 2022.
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ConFoc: Content-Focus Protection Against Trojan Attacks on Neural Networks. [pdf]
- Miguel Villarreal-Vasquez, and Bharat Bhargava. arXiv, 2021.
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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.
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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.
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Data-free Backdoor Removal based on Channel Lipschitzness. [pdf] [code]
- Runkai Zheng, Rongjun Tang, Jianze Li, and Li Liu. ECCV, 2022.
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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.
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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.
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Boundary augment: A data augment method to defend poison attack. [link]
- Xuan Chen, Yuena Ma, Shiwei Lu, and Yu Yao. IET Image Processing, 2021.
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Adversarial Neuron Pruning Purifies Backdoored Deep Models. [pdf] [code]
- Dongxian Wu and Yisen Wang. NeurIPS, 2021.
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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.
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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.
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Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks. [pdf] [code]
- Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg. RAID, 2018.
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Neural Trojans. [pdf]
- Yuntao Liu, Yang Xie, and Ankur Srivastava. ICCD, 2017.
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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.
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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.
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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.
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Defense against Backdoor Attacks via Identifying and Purifying Bad Neurons. [pdf]
- Mingyuan Fan, Yang Liu, Cen Chen, Ximeng Liu, and Wenzhong Guo. arXiv, 2022.
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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.
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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.
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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.
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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.
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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]
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Trigger Hunting with a Topological Prior for Trojan Detection. [pdf]
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Detection of Backdoors in Trained Classifiers Without Access to the Training Set. [pdf]
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Black-box Detection of Backdoor Attacks with Limited Information and Data. [pdf]
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Backdoor Scanning for Deep Neural Networks through K-Arm Optimization. [pdf] [code]
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Towards Inspecting and Eliminating Trojan Backdoors in Deep Neural Networks. [pdf] [previous version] [code]
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GangSweep: Sweep out Neural Backdoors by GAN. [pdf]
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Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks. [pdf] [code]
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Defending Neural Backdoors via Generative Distribution Modeling. [pdf] [code]
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DeepInspect: A Black-box Trojan Detection and Mitigation Framework for Deep Neural Networks. [pdf]
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Identifying Physically Realizable Triggers for Backdoored Face Recognition Networks. [link]
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Revealing Perceptible Backdoors in DNNs Without the Training Set via the Maximum Achievable Misclassification Fraction Statistic. [pdf]
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Confidence Matters: Inspecting Backdoors in Deep Neural Networks via Distribution Transfer. [pdf]
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One-shot Neural Backdoor Erasing via Adversarial Weight Masking. [pdf]
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Defense Against Multi-target Trojan Attacks. [pdf]
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Model-Contrastive Learning for Backdoor Defense. [pdf]
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Backdoor Defense with Machine Unlearning. [pdf]
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CatchBackdoor: Backdoor Testing by Critical Trojan Neural Path Identification via Differential Fuzzing. [pdf]
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Detect and Remove Watermark in Deep Neural Networks via Generative Adversarial Networks. [pdf]
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TAD: Trigger Approximation based Black-box Trojan Detection for AI. [pdf]
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Scalable Backdoor Detection in Neural Networks. [pdf]
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NNoculation: Broad Spectrum and Targeted Treatment of Backdoored DNNs. [pdf] [code]
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Complex Backdoor Detection by Symmetric Feature Differencing. [pdf] [code]
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Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios. [pdf] [code]
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An Anomaly Detection Approach for Backdoored Neural Networks: Face Recognition as a Case Study. [pdf]
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Detecting AI Trojans Using Meta Neural Analysis. [pdf]
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Topological Detection of Trojaned Neural Networks. [pdf]
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Black-box Detection of Backdoor Attacks with Limited Information and Data. [pdf]
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Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs. [pdf] [code]
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One-Pixel Signature: Characterizing CNN Models for Backdoor Detection. [pdf]
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Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases. [pdf] [code]
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Detecting Backdoor Attacks via Class Difference in Deep Neural Networks. [pdf]
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Baseline Pruning-Based Approach to Trojan Detection in Neural Networks. [pdf]
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Attention Hijacking in Trojan Transformers. [pdf]
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Universal Post-Training Backdoor Detection. [pdf]
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Trojan Signatures in DNN Weights. [pdf]
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EX-RAY: Distinguishing Injected Backdoor from Natural Features in Neural Networks by Examining Differential Feature Symmetry. [pdf]
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TOP: Backdoor Detection in Neural Networks via Transferability of Perturbation. [pdf]
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Detecting Trojaned DNNs Using Counterfactual Attributions. [pdf]
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Adversarial examples are useful too! [pdf] [code]
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Cassandra: Detecting Trojaned Networks from Adversarial Perturbations. [pdf]
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Odyssey: Creation, Analysis and Detection of Trojan Models. [pdf] [dataset]
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Noise-response Analysis for Rapid Detection of Backdoors in Deep Neural Networks. [pdf]
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NeuronInspect: Detecting Backdoors in Neural Networks via Output Explanations. [pdf]
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Backdoor Defense via Decoupling the Training Process. [pdf] [code]
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Anti-Backdoor Learning: Training Clean Models on Poisoned Data. [pdf] [code]
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Robust Anomaly Detection and Backdoor Attack Detection via Differential Privacy. [pdf] [code]
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Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Trade-off. [pdf]
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What Doesn't Kill You Makes You Robust(er): Adversarial Training against Poisons and Backdoors. [pdf]
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Removing Backdoor-Based Watermarks in Neural Networks with Limited Data. [pdf]
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On the Effectiveness of Adversarial Training against Backdoor Attacks. [pdf]
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Resurrecting Trust in Facial Recognition: Mitigating Backdoor Attacks in Face Recognition to Prevent Potential Privacy Breaches. [pdf]
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SanitAIs: Unsupervised Data Augmentation to Sanitize Trojaned Neural Networks. [pdf]
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On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping. [pdf] [code]
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DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations. [pdf]
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Can We Mitigate Backdoor Attack Using Adversarial Detection Methods? [link]
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LinkBreaker: Breaking the Backdoor-Trigger Link in DNNs via Neurons Consistency Check. [link]
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Similarity-based Integrity Protection for Deep Learning Systems. [link]
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Rethinking the Backdoor Attacks' Triggers: A Frequency Perspective. [pdf] [code]
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Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection. [pdf] [code]
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SPECTRE: Defending Against Backdoor Attacks Using Robust Statistics. [pdf] [code]
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A Feature-Based On-Line Detector to Remove Adversarial-Backdoors by Iterative Demarcation. [pdf]
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CLEANN: Accelerated Trojan Shield for Embedded Neural Networks. [pdf]
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Robust Anomaly Detection and Backdoor Attack Detection via Differential Privacy. [pdf] [code]
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Simple, Attack-Agnostic Defense Against Targeted Training Set Attacks Using Cosine Similarity. [pdf] [code]
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SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems. [pdf]
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STRIP: A Defence Against Trojan Attacks on Deep Neural Networks. [pdf] [extension] [code]
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Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering. [pdf] [code]
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Deep Probabilistic Models to Detect Data Poisoning Attacks. [pdf]
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Spectral Signatures in Backdoor Attacks. [pdf] [code]
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Fight Poison with Poison: Detecting Backdoor Poison Samples via Decoupling Benign Correlations. [pdf] [code]
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PiDAn: A Coherence Optimization Approach for Backdoor Attack Detection and Mitigation in Deep Neural Networks. [pdf]
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Towards Effective and Robust Neural Trojan Defenses via Input Filtering. [pdf]
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Neural Network Trojans Analysis and Mitigation from the Input Domain. [pdf]
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A General Framework for Defending Against Backdoor Attacks via Influence Graph. [pdf]
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NTD: Non-Transferability Enabled Backdoor Detection. [pdf]
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A Unified Framework for Task-Driven Data Quality Management. [pdf]
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TESDA: Transform Enabled Statistical Detection of Attacks in Deep Neural Networks. [pdf]
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Traceback of Data Poisoning Attacks in Neural Networks. [pdf]
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Provable Guarantees against Data Poisoning Using Self-Expansion and Compatibility. [pdf]
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Online Defense of Trojaned Models using Misattributions. [pdf]
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Detecting Backdoor in Deep Neural Networks via Intentional Adversarial Perturbations. [pdf]
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Exposing Backdoors in Robust Machine Learning Models. [pdf]
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HaS-Nets: A Heal and Select Mechanism to Defend DNNs Against Backdoor Attacks for Data Collection Scenarios. [pdf]
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RAB: Provable Robustness Against Backdoor Attacks. [pdf] [code]
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Certified Robustness of Nearest Neighbors against Data Poisoning and Backdoor Attacks. [pdf]
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Deep Partition Aggregation: Provable Defense against General Poisoning Attacks [pdf] [code]
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Intrinsic Certified Robustness of Bagging against Data Poisoning Attacks [pdf] [code]
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Certified Robustness to Label-Flipping Attacks via Randomized Smoothing. [pdf]
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On Certifying Robustness against Backdoor Attacks via Randomized Smoothing. [pdf]
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Secure Partial Aggregation: Making Federated Learning More Robust for Industry 4.0 Applications. [link]
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Mitigating the Backdoor Attack by Federated Filters for Industrial IoT Applications. [link]
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Neurotoxin: Durable Backdoors in Federated Learning. [pdf]
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FLAME: Taming Backdoors in Federated Learning. [pdf]
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Defending Label Inference and Backdoor Attacks in Vertical Federated Learning. [pdf]
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An Analysis of Byzantine-Tolerant Aggregation Mechanisms on Model Poisoning in Federated Learning. [link]
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Against Backdoor Attacks In Federated Learning With Differential Privacy. [link]
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Stability-Based Analysis and Defense against Backdoor Attacks on Edge Computing Services. [link]
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CRFL: Certifiably Robust Federated Learning against Backdoor Attacks. [pdf]
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Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning. [pdf]
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Attack of the Tails: Yes, You Really Can Backdoor Federated Learning. [pdf]
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DBA: Distributed Backdoor Attacks against Federated Learning. [pdf]
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Defending Against Backdoors in Federated Learning with Robust Learning Rate. [pdf]
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PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion. [pdf]
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The Limitations of Federated Learning in Sybil Settings. [pdf] [extension] [code]
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BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning. [pdf]
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Backdoor Attacks and Defenses in Feature-partitioned Collaborative Learning. [pdf]
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Assisting Backdoor Federated Learning with Whole Population Knowledge Alignment. [pdf]
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FL-Defender: Combating Targeted Attacks in Federated Learning. [pdf]
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Backdoor Attack is A Devil in Federated GAN-based Medical Image Synthesis. [pdf]
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Backdoors Stuck at The Frontdoor: Multi-Agent Backdoor Attacks That Backfire. [pdf]
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FLGUARD: Secure and Private Federated Learning. [pdf]
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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.
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Backdoor Attacks on Federated Meta-Learning. [pdf]
- Chien-Lun Chen, Leana Golubchik, and Marco Paolieri. arXiv, 2020.
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Dynamic backdoor attacks against federated learning. [pdf]
- Anbu Huang. arXiv, 2020.
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Federated Learning in Adversarial Settings. [pdf]
- Raouf Kerkouche, Gergely Ács, and Claude Castelluccia. arXiv, 2020.
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BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecture. [pdf]
- Harsh Bimal Desai, Mustafa Safa Ozdayi, and Murat Kantarcioglu. arXiv, 2020.
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Mitigating Backdoor Attacks in Federated Learning. [pdf]
- Chen Wu, Xian Yang, Sencun Zhu, and Prasenjit Mitra. arXiv, 2020.
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BaFFLe: Backdoor detection via Feedback-based Federated Learning. [pdf]
- ebastien Andreina, Giorgia Azzurra Marson, Helen Möllering, and Ghassan Karame. arXiv, 2020.
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Learning to Detect Malicious Clients for Robust Federated Learning. [pdf]
- Suyi Li, Yong Cheng, Wei Wang, Yang Liu, and Tianjian Chen. arXiv, 2020.
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Attack-Resistant Federated Learning with Residual-based Reweighting. [pdf] [code]
- Shuhao Fu, Chulin Xie, Bo Li, and Qifeng Chen. arXiv, 2019.
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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.
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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.
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Hidden Trigger Backdoor Attacks. [pdf] [code]
- Aniruddha Saha, Akshayvarun Subramanya, and Hamed Pirsiavash. AAAI, 2020.
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Weight Poisoning Attacks on Pre-trained Models. [pdf] [code]
- Keita Kurita, Paul Michel, and Graham Neubig. ACL, 2020.
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Latent Backdoor Attacks on Deep Neural Networks. [pdf]
- Yuanshun Yao, Huiying Li, Haitao Zheng and Ben Y. Zhao. CCS, 2019.
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Architectural Backdoors in Neural Networks. [pdf]
- Mikel Bober-Irizar, Ilia Shumailov, Yiren Zhao, Robert Mullins, and Nicolas Papernot. arXiv, 2022.
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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.
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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.
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BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning. [pdf]
- Lun Wang, Zaynah Javed, Xian Wu, Wenbo Guo, Xinyu Xing, and Dawn Song. IJCAI, 2021.
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TrojDRL: Evaluation of Backdoor Attacks on Deep Reinforcement Learning. [pdf]
- Panagiota Kiourti, Kacper Wardega, Susmit Jha, and Wenchao Li. DAC, 2020.
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Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers. [pdf]
- Chace Ashcraft and Kiran Karra. ICLR Workshop, 2021.
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A Temporal-Pattern Backdoor Attack to Deep Reinforcement Learning. [pdf]
- Yinbo Yu, Jiajia Liu, Shouqing Li, Kepu Huang, and Xudong Feng. arXiv, 2022.
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Backdoor Detection in Reinforcement Learning. [pdf]
- Junfeng Guo, Ang Li, and Cong Liu. arXiv, 2022.
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Design of Intentional Backdoors in Sequential Models. [pdf]
- Zhaoyuan Yang, Naresh Iyer, Johan Reimann, and Nurali Virani. arXiv, 2019.
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Backdoor Attacks on Self-Supervised Learning. [pdf] [code]
- Aniruddha Saha, Ajinkya Tejankar, Soroush Abbasi Koohpayegani, and Hamed Pirsiavash. CVPR, 2022.
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Poisoning and Backdooring Contrastive Learning. [pdf]
- Nicholas Carlini and Andreas Terzis. ICLR, 2022.
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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.
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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.
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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.
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Watermarking Pre-trained Encoders in Contrastive Learning. [pdf]
- Yutong Wu, Han Qiu, Tianwei Zhang, Jiwei Li, and Meikang Qiu. arXiv, 2022.
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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.
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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.
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Understanding the Threats of Trojaned Quantized Neural Network in Model Supply Chains. [pdf]
- Xudong Pan, Mi Zhang, Yifan Yan, and Min Yang. ACSAC, 2021
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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.
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Stealthy Backdoors as Compression Artifacts. [pdf]
- Yulong Tian, Fnu Suya, Fengyuan Xu, and David Evans. arXiv, 2021.
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Spinning Language Models: Risks of Propaganda-as-a-Service and Countermeasures [pdf] [code]
- Eugene Bagdasaryan and Vitaly Shmatikov. IEEE S&P, 2022
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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.
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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.
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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.
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A Study of the Attention Abnormality in Trojaned BERTs. [pdf] [code]
- Weimin Lyu, Songzhu Zheng, Tengfei Ma, and Chao Chen. NAACL, 2022.
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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.
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BDDR: An Effective Defense Against Textual Backdoor Attacks. [pdf]
- Kun Shao, Junan Yang, Yang Ai, Hui Liu, and Yu Zhang. Computers & Security, 2021.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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ONION: A Simple and Effective Defense Against Textual Backdoor Attacks. [pdf]
- Fanchao Qi, Yangyi Chen, Mukai Li, Zhiyuan Liu, and Maosong Sun. EMNLP, 2021.
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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.
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Rethinking Stealthiness of Backdoor Attack against NLP Models. [pdf] [code]
- Wenkai Yang, Yankai Lin, Peng Li, Jie Zhou, and Xu Sun. ACL, 2021.
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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.
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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.
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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.
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BFClass: A Backdoor-free Text Classification Framework. [pdf] [code]
- Zichao Li, Dheeraj Mekala, Chengyu Dong, and Jingbo Shang. EMNLP-Findings, 2021.
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Weight Poisoning Attacks on Pre-trained Models. [pdf] [code]
- Keita Kurita, Paul Michel, and Graham Neubig. ACL, 2020.
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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.
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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.
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Textual Backdoor Attack for the Text Classification System. [pdf]
- Hyun Kwon and Sanghyun Lee. Security and Communication Networks, 2021.
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A Backdoor Attack Against LSTM-based Text Classification Systems. [pdf]
- Jiazhu Dai, Chuanshuai Chen, and Yufeng Li. IEEE Access, 2019.
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Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder. [pdf]
- Alvin Chan, Yi Tay, Yew-Soon Ong, and Aston Zhang. EMNLP-Findings, 2020.
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PerD: Perturbation Sensitivity-based Neural Trojan Detection Framework on NLP Applications. [pdf]
- Diego Garcia-soto, Huili Chen, and Farinaz Koushanfar. arXiv, 2022.
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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.
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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.
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Textual Backdoor Attacks with Iterative Trigger Injection. [pdf] [code]
- Jun Yan, Vansh Gupta, and Xiang Ren. arXiv, 2022.
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WeDef: Weakly Supervised Backdoor Defense for Text Classification. [pdf]
- Lesheng Jin, Zihan Wang, and Jingbo Shang. arXiv, 2022.
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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.
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Rethink Stealthy Backdoor Attacks in Natural Language Processing. [pdf]
- Lingfeng Shen, Haiyun Jiang, Lemao Liu, and Shuming Shi. arXiv, 2022.
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Textual Backdoor Attacks Can Be More Harmful via Two Simple Tricks. [pdf]
- Yangyi Chen, Fanchao Qi, Zhiyuan Liu, and Maosong Sun. arXiv, 2021.
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Spinning Sequence-to-Sequence Models with Meta-Backdoors. [pdf]
- Eugene Bagdasaryan and Vitaly Shmatikov. arXiv, 2021.
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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.
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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.
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Detecting Universal Trigger’s Adversarial Attack with Honeypot. [pdf]
- Thai Le, Noseong Park, Dongwon Lee. arXiv, 2020.
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Mitigating Backdoor Attacks in LSTM-based Text Classification Systems by Backdoor Keyword Identification. [pdf]
- Chuanshuai Chen, and Jiazhu Dai. arXiv, 2020.
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Trojaning Language Models for Fun and Profit. [pdf]
- Xinyang Zhang, Zheng Zhang, and Ting Wang. arXiv, 2020.
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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.
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- Zhaohan Xi, Ren Pang, Shouling Ji, and Ting Wang. USENIX Security, 2021.
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Backdoor Attacks to Graph Neural Networks. [pdf]
- Zaixi Zhang, Jinyuan Jia, Binghui Wang, and Neil Zhenqiang Gong. SACMAT, 2021.
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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.
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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.
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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.
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Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction. [pdf]
- Jinyin Chen, Haiyang Xiong, Haibin Zheng, Jian Zhang, Guodong Jiang, and Yi Liu. arXiv, 2021.
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Explainability-based Backdoor Attacks Against Graph Neural Networks. [pdf]
- Jing Xu, Minhui, Xue, and Stjepan Picek. arXiv, 2021.
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A Backdoor Attack against 3D Point Cloud Classifiers. [pdf] [code]
- Zhen Xiang, David J. Miller, Siheng Chen, Xi Li, and George Kesidis. ICCV, 2021.
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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.
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Imperceptible and Robust Backdoor Attack in 3D Point Cloud. [pdf]
- Kuofeng Gao, Jiawang Bai, Baoyuan Wu, Mengxi Ya, and Shu-Tao Xia. arXiv, 2022.
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Detecting Backdoor Attacks Against Point Cloud Classifiers. [pdf]
- Zhen Xiang, David J. Miller, Siheng Chen, Xi Li, and George Kesidis. arXiv, 2021.
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Poisoning MorphNet for Clean-Label Backdoor Attack to Point Clouds. [pdf]
- Guiyu Tian, Wenhao Jiang, Wei Liu, and Yadong Mu. arXiv, 2021.
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Backdoor Attack against Speaker Verification [pdf] [code]
- Tongqing Zhai, Yiming Li, Ziqi Zhang, Baoyuan Wu, Yong Jiang, and Shu-Tao Xia. ICASSP, 2021.
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DriNet: Dynamic Backdoor Attack against Automatic Speech Recognization Models. [link]
- Jianbin Ye, Xiaoyuan Liu, Zheng You, Guowei Li, and Bo Liu. Applied Sciences, 2022.
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Can You Hear It? Backdoor Attacks via Ultrasonic Triggers. [pdf] [code]
- Stefanos Koffas, Jing Xu, Mauro Conti, and Stjepan Picek. WiseML, 2022.
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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.
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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.
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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.
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Explainability Matters: Backdoor Attacks on Medical Imaging. [pdf]
- Munachiso Nwadike, Takumi Miyawaki, Esha Sarkar, Michail Maniatakos, and Farah Shamout. AAAI Workshop, 2021.
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TRAPDOOR: Repurposing Backdoors to Detect Dataset Bias in Machine Learning-based Genomic Analysis. [pdf]
- Esha Sarkar and Michail Maniatakos. arXiv, 2021.
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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.
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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.
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Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers. [pdf]
- Giorgio Severi, Jim Meyer, Scott Coull, and Alina Oprea. USENIX Security, 2021.
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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.
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Few-Shot Backdoor Attacks on Visual Object Tracking. [pdf] [code]
- Yiming Li, Haoxiang Zhong, Xingjun Ma, Yong Jiang, and Shu-Tao Xia. ICLR, 2022.
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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.
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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.
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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.
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Backdoor Attacks on the DNN Interpretation System. [pdf]
- Shihong Fang and Anna Choromanska. AAAI, 2022.
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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.
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Object-Oriented Backdoor Attack Against Image Captioning. [link]
- Meiling Li, Nan Zhong, Xinpeng Zhang, Zhenxing Qian, and Sheng Li. ICASSP, 2022.
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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.
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Multi-Target Invisibly Trojaned Networks for Visual Recognition and Detection. [pdf]
- Xinzhe Zhou, Wenhao Jiang, Sheng Qi, and Yadong Mu. IJCAI, 2021.
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Hidden Backdoor Attack against Semantic Segmentation Models. [pdf]
- Yiming Li, Yanjie Li, Yalei Lv, Yong Jiang, and Shu-Tao Xia. ICLR Workshop, 2021.
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Adversarial Targeted Forgetting in Regularization and Generative Based Continual Learning Models. [link]
- Muhammad Umer and Robi Polikar. IJCNN, 2021.
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Targeted Forgetting and False Memory Formation in Continual Learners through Adversarial Backdoor Attacks. [pdf]
- Muhammad Umer, Glenn Dawson, Robi Polikar. IJCNN, 2020.
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Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learning. [pdf]
- Kemal Davaslioglu, and Yalin E. Sagduyu. DySPAN, 2019.
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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.
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A Temporal Chrominance Trigger for Clean-label Backdoor Attack against Anti-spoof Rebroadcast Detection. [pdf]
- Wei Guo, Benedetta Tondi, and Mauro Barni. arXiv, 2022.
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BadDet: Backdoor Attacks on Object Detection. [pdf]
- Shih-Han Chan, Yinpeng Dong, Jun Zhu, Xiaolu Zhang, and Jun Zhou. arXiv, 2022.
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Backdoor Attacks on Bayesian Neural Networks using Reverse Distribution. [pdf]
- Zhixin Pan and Prabhat Mishra. arXiv, 2022.
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Backdooring Explainable Machine Learning. [pdf]
- Maximilian Noppel, Lukas Peter, and Christian Wressnegger. arXiv, 2022.
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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.
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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.
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Targeted Trojan-Horse Attacks on Language-based Image Retrieval. [pdf]
- Fan Hu, Aozhu Chen, and Xirong Li. arXiv, 2022.
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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.
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Dual-Key Multimodal Backdoors for Visual Question Answering. [pdf]
- Matthew Walmer, Karan Sikka, Indranil Sur, Abhinav Shrivastava, and Susmit Jha. arXiv, 2021.
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Clean-label Backdoor Attack against Deep Hashing based Retrieval. [pdf]
- Kuofeng Gao, Jiawang Bai, Bin Chen, Dongxian Wu, and Shu-Tao Xia. arXiv, 2021.
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Backdoor Attacks on Network Certification via Data Poisoning. [pdf]
- Tobias Lorenz, Marta Kwiatkowska, and Mario Fritz. arXiv, 2021.
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Backdoor Attack and Defense for Deep Regression. [pdf]
- Xi Li, George Kesidis, David J. Miller, and Vladimir Lucic. arXiv, 2021.
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The Devil is in the GAN: Defending Deep Generative Models Against Backdoor Attacks. [pdf]
- Ambrish Rawat, Killian Levacher, and Mathieu Sinn. arXiv, 2021.
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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.
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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.
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Backdoors in Neural Models of Source Code. [pdf]
- Goutham Ramakrishnan, and Aws Albarghouthi. arXiv, 2020.
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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.
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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.
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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.
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Backdoor Defense via Decoupling the Training Process. [pdf] [code]
- Kunzhe Huang, Yiming Li, Baoyuan Wu, Zhan Qin, and Kui Ren. ICLR, 2022.
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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.
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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)
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Susceptibility & Defense of Satellite Image-trained Convolutional Networks to Backdoor Attacks. [link]
- Ethan Brewer, Jason Lin, and Dan Runfola. Information Sciences, 2021.
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Data-Efficient Backdoor Attacks. [pdf] [code]
- Pengfei Xia, Ziqiang Li, Wei Zhang, and Bin Li. IJCAI, 2022.
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Excess Capacity and Backdoor Poisoning. [pdf]
- Naren Sarayu Manoj and Avrim Blum. NeurIPS, 2021.
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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.
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Rethinking the Backdoor Attacks' Triggers: A Frequency Perspective. [pdf]
- Yi Zeng, Won Park, Z. Morley Mao, and Ruoxi Jia. ICCV, 2021.
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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.
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Can Optical Trojans Assist Adversarial Perturbations? [pdf]
- Adith Boloor, Tong Wu, Patrick Naughton, Ayan Chakrabarti, Xuan Zhang, and Yevgeniy Vorobeychik. ICCV Workshop, 2021.
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On the Trade-off between Adversarial and Backdoor Robustness. [pdf]
- Cheng-Hsin Weng, Yan-Ting Lee, and Shan-Hung Wu. NeurIPS, 2020.
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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.
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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.
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On Evaluating Neural Network Backdoor Defenses. [pdf]
- Akshaj Veldanda, and Siddharth Garg. NeurIPS Workshop, 2020.
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Attention Hijacking in Trojan Transformers. [pdf]
- Weimin Lyu, Songzhu Zheng, Tengfei Ma, Haibin Ling, and Chao Chen. arXiv, 2022.
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Game of Trojans: A Submodular Byzantine Approach. [pdf]
- Dinuka Sahabandu, Arezoo Rajabi, Luyao Niu, Bo Li, Bhaskar Ramasubramanian, and Radha Poovendran. arXiv, 2022.
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BackdoorBench: A Comprehensive Benchmark of Backdoor Learning. [pdf] [website]
- Baoyuan Wu, Hongrui Chen, Mingda Zhang, Zihao Zhu, Shaokui Wei, Danni Yuan, Chao Shen, and Hongyuan Zha. arXiv, 2022.
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Auditing Visualizations: Transparency Methods Struggle to Detect Anomalous Behavior. [pdf] [code]
- Jean-Stanislas Denain and Jacob Steinhardt. arXiv, 2022.
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Natural Backdoor Datasets. [pdf]
- Emily Wenger, Roma Bhattacharjee, Arjun Nitin Bhagoji, Josephine Passananti, Emilio Andere, Haitao Zheng, and Ben Y. Zhao. arXiv, 2022.
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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.
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Can Backdoor Attacks Survive Time-Varying Models? [pdf]
- Huiying Li, Arjun Nitin Bhagoji, Ben Y. Zhao, and Haitao Zheng. arXiv, 2022.
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Dynamic Backdoor Attacks with Global Average Pooling [pdf] [code]
- Stefanos Koffas, Stjepan Picek, and Mauro Conti. arXiv, 2022.
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Planting Undetectable Backdoors in Machine Learning Models. [pdf]
- Shafi Goldwasser, Michael P. Kim, Vinod Vaikuntanathan, and Or Zamir. arXiv, 2022.
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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.
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Neural Network Trojans Analysis and Mitigation from the Input Domain. [pdf]
- Zhenting Wang, Hailun Ding, Juan Zhai, and Shiqing Ma. arXiv, 2022.
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Widen The Backdoor To Let More Attackers In. [pdf]
- Siddhartha Datta, Giulio Lovisotto, Ivan Martinovic, and Nigel Shadbolt. arXiv, 2021.
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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.
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Rethinking the Trigger of Backdoor Attack. [pdf]
- Yiming Li, Tongqing Zhai, Baoyuan Wu, Yong Jiang, Zhifeng Li, and Shutao Xia. arXiv, 2020.
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Poisoned Classifiers are Not Only Backdoored, They are Fundamentally Broken. [pdf] [code]
- Mingjie Sun, Siddhant Agarwal, and J. Zico Kolter. ICLR Workshop, 2021.
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Effect of Backdoor Attacks over the Complexity of the Latent Space Distribution. [pdf] [code]
- Henry D. Chacon, and Paul Rad. arXiv, 2020.
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Trembling Triggers: Exploring the Sensitivity of Backdoors in DNN-based Face Recognition. [pdf]
- Cecilia Pasquini, and Rainer Böhme. EURASIP Journal on Information Security, 2020.
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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.
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Membership Inference via Backdooring. [pdf] [code]
- Hongsheng Hu, Zoran Salcic, Gillian Dobbie, Jinjun Chen, Lichao Sun, and Xuyun Zhang. IJCAI, 2022.
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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.
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One Step Further: Evaluating Interpreters using Metamorphic Testing. [pdf]
- Ming Fan, Jiali Wei, Wuxia Jin, Zhou Xu, Wenying Wei, and Ting Liu. ISSTA, 2022.
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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.
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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])
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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.
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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.
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Protecting Deep Cerebrospinal Fluid Cell Image Processing Models with Backdoor and Semi-Distillation. [link]
- FangQi Li, Shilin Wang, and Zhenhai Wang. DICTA, 2021.
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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.
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Watermarking Graph Neural Networks based on Backdoor Attacks. [pdf]
- Jing Xu and Stjepan Picek. arXiv, 2021.
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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.
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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.
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A Stealthy and Robust Fingerprinting Scheme for Generative Models. [pdf]
- Guanlin Li, Shangwei Guo, Run Wang, Guowen Xu, and Tianwei Zhang. arXiv, 2021.
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Towards Probabilistic Verification of Machine Unlearning. [pdf] [code]
- David Marco Sommer, Liwei Song, Sameer Wagh, and Prateek Mittal. arXiv, 2020.