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It has been widely observed that deep neural networks (DNN) are vulnerable to backdoor attacks where attackers could manipulate the model behavior maliciously by tampering with a small set of training samples.
Learning multiple layers of features from tiny images
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Fine-pruning: Defending against backdooring attacks on deep neural networks
Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2018
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A new backdoor attack in cnns by training set corruption without label poisoning
Mauro Barni, Kassem Kallas, and Benedetta Tondi · 2019
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Badnets: Evaluating backdooring attacks on deep neural networks
Tianyu Gu, Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2019
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Label-consistent backdoor attacks
Alexander Turner, Dimitris Tsipras, and Aleksander Madry · 2019
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Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y Zhao · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Input-aware dynamic backdoor attack
Tuan Anh Nguyen and Anh Tran · 2020
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Bypassing backdoor detection algorithms in deep learning
Reza Shokri et al · 2020
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Poisoning and backdooring contrastive learning
Nicholas Carlini and Andreas Terzis · 2021
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Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization
John P Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, and Ludwig Schmidt · 2021
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Wanet - imperceptible warping-based backdoor attack
Tuan Anh Nguyen and Anh Tuan Tran · 2021
Back to the drawing board: A critical evaluation of poisoning attacks on production federated learning
Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, and Daniel Ramage · 2022
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V Le · 2022
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Backdoorbench: A comprehensive benchmark of backdoor learning
Baoyuan Wu, Hongrui Chen, Mingda Zhang, Zihao Zhu, Shaokui Wei, Danni Yuan, and Chao Shen · 2022
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Adversarial unlearning of backdoors via implicit hypergradient
Yi Zeng, Si Chen, Won Park, Zhuoqing Mao, Ming Jin, and Ruoxi Jia · 2022
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Fine-tuning global model via data-free knowledge distillation for non-iid federated learning
Lin Zhang, Li Shen, Liang Ding, Dacheng Tao, and Ling-Yu Duan · 2022
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Combined scaling for zero-shot transfer learning
Hieu Pham, Zihang Dai, Golnaz Ghiasi, Hanxiao Liu, Adams Wei Yu, Minh-Thang Luong, Mingxing Tan, and Quoc V Le · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Adversarial neuron pruning purifies backdoored deep models
Dongxian Wu and Yisen Wang · 2021
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Poisoning and backdooring contrastive learning
Nicholas Carlini and Andreas Terzis · 2022
Cited alongside, same era.
Dataset security for machine learning: Data poisoning, backdoor attacks, and defenses
Micah Goldblum, Dimitris Tsipras, Chulin Xie, Xinyun Chen, Avi Schwarzschild, Dawn Song, Aleksander Madry, Bo Li, and Tom Goldstein · 2022
Cited alongside, same era.
Backdoor defense via decoupling the training process
Kunzhe Huang, Yiming Li, Baoyuan Wu, Zhan Qin, and Kui Ren · 2022
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Data-free backdoor removal based on channel lipschitzness
Runkai Zheng, Rongjun Tang, Jianze Li, and Li Liu · 2022
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Poisoning web-scale training datasets is practical
Nicholas Carlini, Matthew Jagielski, Christopher A Choquette-Choo, Daniel Paleka, Will Pearce, Hyrum Anderson, Andreas Terzis, Kurt Thomas, and Florian Tramèr · 2023
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Towards understanding feature learning in out-of-distribution generalization
Yongqiang Chen, Wei Huang, Kaiwen Zhou, Yatao Bian, Bo Han, and James Cheng · 2023
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Spurious feature diversification improves out-of-distribution generalization
Yong Lin, Lu Tan, Yifan Hao, Honam Wong, Hanze Dong, Weizhong Zhang, Yujiu Yang, and Tong Zhang · 2023
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Revisiting the assumption of latent separability for backdoor defenses
Xiangyu Qi, Tinghao Xie, Yiming Li, Saeed Mahloujifar, and Prateek Mittal · 2023
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Revisiting personalized federated learning: Robustness against backdoor attacks
Zeyu Qin, Liuyi Yao, Daoyuan Chen, Yaliang Li, Bolin Ding, and Minhao Cheng · 2023
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Learning useful representations for shifting tasks and distributions
Jianyu Zhang and Léon Bottou · 2023
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Enhancing fine-tuning based backdoor defense with sharpness-aware minimization
Mingli Zhu, Shaokui Wei, Li Shen, Yanbo Fan, and Baoyuan Wu · 2023
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