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Deep neural networks (DNNs) are vulnerable to "backdoor" poisoning attacks, in which an adversary implants a secret trigger into an otherwise normally functioning model.
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Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
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Mihalj Bakator and Dragica Radosav · 2018
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Detecting backdoor attacks on deep neural networks by activation clustering, 2018
Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Benjamin Edwards, Taesung Lee, Ian Molloy, and Biplav Srivastava · 2018
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Fine-pruning: Defending against backdooring attacks on deep neural networks, 2018
Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2018
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Trojaning attack on neural networks
Yingqi Liu, Shiqing Ma, Yousra Aafer, W. Lee, Juan Zhai, Weihang Wang, and X. Zhang · 2018
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Towards deep learning models resistant to adversarial attacks
A. Madry, Aleksandar Makelov, L. Schmidt, D. Tsipras, and Adrian Vladu · 2018
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Clean-label backdoor attacks
A. Turner, D. Tsipras, and A. Madry · 2018
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Backdoor attacks and countermeasures on deep learning: A comprehensive review, 2020
Yansong Gao, Bao Gia Doan, Zhi Zhang, Siqi Ma, Jiliang Zhang, Anmin Fu, Surya Nepal, and Hyoungshick Kim · 2020
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Strip: A defence against trojan attacks on deep neural networks, 2020
Yansong Gao, Chang Xu, Derui Wang, Shiping Chen, Damith C. Ranasinghe, and Surya Nepal · 2020
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Intelligence advanced research projects agency: Trojans in artificial intelligence (trojai)
IARPA · 2020
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Matthijs F. Jansen, V. Codreanu, and Ana-Lucia Varbanescu · 2020
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The trojai software framework: An opensource tool for embedding trojans into deep learning models, 2020
Kiran Karra, Chace Ashcraft, and Neil Fendley · 2020
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Huili Chen, Cheng Fu, Jishen Zhao, and Farinaz Koushanfar · 2019
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AdverTorch v0.1: An adversarial robustness toolbox based on pytorch
Gavin Weiguang Ding, Luyu Wang, and Xiaomeng Jin · 2019
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Badnets: Identifying vulnerabilities in the machine learning model supply chain, 2019
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2019
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Tabor: A highly accurate approach to inspecting and restoring trojan backdoors in ai systems, 2019
Wenbo Guo, Lun Wang, Xinyu Xing, Min Du, and Dawn Song · 2019
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Nic: Detecting adversarial samples with neural network invariant checking
Shiqing Ma, Yingqi Liu, G. Tao, W. Lee, and X. Zhang · 2019
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Hidden trigger backdoor attacks, 2019
Aniruddha Saha, Akshayvarun Subramanya, and Hamed Pirsiavash · 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 Zhao · 2019
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A survey on neural trojans
Y. Liu, A. Mondal, A. Chakraborty, M. Zuzak, N. Jacobsen, D. Xing, and A. Srivastava · 2020
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Poisoned classifiers are not only backdoored, they are fundamentally broken, 2020
Mingjie Sun, Siddhant Agarwal, and J. Zico Kolter · 2020
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Practical detection of trojan neural networks: Data-limited and data-free cases, 2020
Ren Wang, Gaoyuan Zhang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong, and Meng Wang · 2020
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Detection of backdoors in trained classifiers without access to the training set, 2020
Zhen Xiang, David J. Miller, and George Kesidis · 2020
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Deepsweep: An evaluation framework for mitigating dnn backdoor attacks using data augmentation, 2020
Yi Zeng, Han Qiu, Shangwei Guo, Tianwei Zhang, Meikang Qiu, and Bhavani Thuraisingham · 2020
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Baseline pruning-based approach to trojan detection in neural networks, 2021
Peter Bajcsy and Michael Majurski · 2021
Closest in time.
Backdoor learning: A survey, 2021
Yiming Li, Baoyuan Wu, Yong Jiang, Zhifeng Li, and Shu-Tao Xia · 2021
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Backdoor scanning for deep neural networks through k-arm optimization, 2021
Guangyu Shen, Yingqi Liu, Guanhong Tao, Shengwei An, Qiuling Xu, Siyuan Cheng, Shiqing Ma, and Xiangyu Zhang · 2021
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