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Deep neural networks (DNNs) are recently shown to be vulnerable to backdoor attacks, where attackers embed hidden backdoors in the DNN model by injecting a few poisoned examples into the training dataset.
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Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 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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Trojaning attack on neural networks
Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and Xiangyu Zhang · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Spectral normalization for generative adversarial networks
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Judea Pearl and Dana Mackenzie · 2018
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Poison frogs! targeted clean-label poisoning attacks on neural networks
Ali Shafahi, W Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein · 2018
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Brandon Tran, Jerry Li, and Aleksander Madry · 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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Detecting backdoor attacks on deep neural networks by activation clustering
Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Benjamin Edwards, Taesung Lee, Ian Molloy, and Biplav Srivastava · 2019
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Deepinspect: A black-box trojan detection and mitigation framework for deep neural networks
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Invisible poisoning: Highly stealthy targeted poisoning attack
Jinyin Chen, Haibin Zheng, Mengmeng Su, Tianyu Du, Changting Lin, and Shouling Ji · 2019
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Strip: A defence against trojan attacks on deep neural networks
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Learning disentangled representation for cross-modal retrieval with deep mutual information estimation
Weikuo Guo, Huaibo Huang, Xiangwei Kong, and Ran He · 2019
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Rare event detection using disentangled representation learning
Ryuhei Hamaguchi, Ken Sakurada, and Ryosuke Nakamura · 2019
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Invisible backdoor attacks on deep neural networks via steganography and regularization
Shaofeng Li, Minhui Xue, Benjamin Zi Hao Zhao, Haojin Zhu, and Xinpeng Zhang · 2019
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Unlearnable examples: Making personal data unexploitable
Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey, and Yisen Wang · 2021
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Disenqnet: Disentangled representation learning for educational questions
Zhenya Huang, Xin Lin, Hao Wang, Qi Liu, Enhong Chen, Jianhui Ma, Yu Su, and Wei Tong · 2021
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Invisible backdoor attack with sample-specific triggers
Yuezun Li, Yiming Li, Baoyuan Wu, Longkang Li, Ran He, and Siwei Lyu · 2021
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Anti-backdoor learning: Training clean models on poisoned data
Yige Li, Xixiang Lyu, Nodens Koren, Lingjuan Lyu, Bo Li, and Xingjun Ma · 2021
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Neural attention distillation: Erasing backdoor triggers from deep neural networks
Yige Li, Xixiang Lyu, Nodens Koren, Lingjuan Lyu, Bo Li, and Xingjun Ma · 2021
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Mitigating confounding bias in recommendation via information bottleneck
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Latent backdoor attacks on deep neural networks
XiaoFeng Wang Lorenzo Cavallaro, Johannes Kinder and Jonathan Katz · 2019
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Clean-label backdoor attacks
Alexander Turner, Dimitris Tsipras, and Aleksander Madry · 2019
Cited alongside, same era.
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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Transferable clean-label poisoning attacks on deep neural nets
Chen Zhu, W Ronny Huang, Hengduo Li, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2019
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Februus: Input purification defense against trojan attacks on deep neural network systems
Bao Gia Doan, Ehsan Abbasnejad, and Damith C Ranasinghe · 2020
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Robust anomaly detection and backdoor attack detection via differential privacy
Min Du, Ruoxi Jia, and Dawn Song · 2020
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Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann · 2020
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Dugang Liu, Pengxiang Cheng, Hong Zhu, Zhenhua Dong, Xiuqiang He, Weike Pan, and Zhong Ming · 2021
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Efficient training of visual transformers with small datasets
Yahui Liu, Enver Sangineto, Wei Bi, Nicu Sebe, Bruno Lepri, and Marco Nadai · 2021
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Wanet–imperceptible warping-based backdoor attack
Anh Nguyen and Anh Tran · 2021
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Counterfactual vqa: A cause-effect look at language bias
Yulei Niu, Kaihua Tang, Hanwang Zhang, Zhiwu Lu, Xian-Sheng Hua, and Ji-Rong Wen · 2021
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Toward causal representation learning
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio · 2021
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Backdoor scanning for deep neural networks through k-arm optimization
Guangyu Shen, Yingqi Liu, Guanhong Tao, Shengwei An, Qiuling Xu, Siyuan Cheng, Shiqing Ma, and Xiangyu Zhang · 2021
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Demon in the variant: Statistical analysis of dnns for robust backdoor contamination detection
Di Tang, XiaoFeng Wang, Haixu Tang, and Kehuan Zhang · 2021
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Detecting ai trojans using meta neural analysis
Xiaojun Xu, Qi Wang, Huichen Li, Nikita Borisov, Carl A Gunter, and Bo Li · 2021
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Backdoor attacks to graph neural networks
Zaixi Zhang, Jinyuan Jia, Binghui Wang, and Neil Zhenqiang Gong · 2021
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Graphmi: Extracting private graph data from graph neural networks
Zaixi Zhang, Qi Liu, Zhenya Huang, Hao Wang, Chengqiang Lu, Chuanren Liu, and Enhong Chen · 2021
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Motif-based graph self-supervised learning for molecular property prediction
Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, and Chee-Kong Lee · 2021
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Flcert: Provably secure federated learning against poisoning attacks
Xiaoyu Cao, Zaixi Zhang, Jinyuan Jia, and Neil Zhenqiang Gong · 2022
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Poisoning and backdooring contrastive learning
Nicholas Carlini and Andreas Terzis · 2022
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Deconfounded visual grounding
Jianqiang Huang, Yu Qin, Jiaxin Qi, Qianru Sun, and Hanwang Zhang · 2022
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Backdoor defense via decoupling the training process
Kunzhe Huang, Yiming Li, Baoyuan Wu, Zhan Qin, and Kui Ren · 2022
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Few-shot backdoor attacks on visual object tracking
Yiming Li, Haoxiang Zhong, Xingjun Ma, Yong Jiang, , and Shu-Tao Xia · 2022
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Adversarial robustness through the lens of causality
Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, and Kun Zhang · 2022
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Fldetector: Defending federated learning against model poisoning attacks via detecting malicious clients
Zaixi Zhang, Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong · 2022
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Hierarchical graph transformer with adaptive node sampling
Zaixi Zhang, Qi Liu, Qingyong Hu, and Chee-Kong Lee · 2022
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Model inversion attacks against graph neural networks
Zaixi Zhang, Qi Liu, Zhenya Huang, Hao Wang, Chee-Kong Lee, and Enhong Chen · 2022
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Protgnn: Towards self-explaining graph neural networks
Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, and Cheekong Lee · 2022
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Graph self-supervised learning for optoelectronic properties of organic semiconductors
Zaixi Zhang, Qi Liu, Shengyu Zhang, Chang-Yu Hsieh, Liang Shi, and Chee-Kong Lee · 2022
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