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Backdoor defense, which aims to detect or mitigate the effect of malicious triggers introduced by attackers, is becoming increasingly critical for machine learning security and integrity.
Flat minima
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The german traffic sign recognition benchmark: a multi-class classification competition
Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel · 2011
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Very deep convolutional networks for large-scale image recognition
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Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 2015
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Deep residual learning for image recognition
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Identity mappings in deep residual networks
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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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 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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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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Iot security techniques based on machine learning: How do iot devices use ai to enhance security?
Liang Xiao, Xiaoyue Wan, Xiaozhen Lu, Yanyong Zhang, and Di Wu · 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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Entropy-sgd: Biasing gradient descent into wide valleys
Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer Chayes, Levent Sagun, and Riccardo Zecchina · 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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Certified adversarial robustness via randomized smoothing
Jeremy Cohen, Elan Rosenfeld, and Zico Kolter · 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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Mrtrix3: A fast, flexible and open software framework for medical image processing and visualisation
J-Donald Tournier, Robert Smith, David Raffelt, Rami Tabbara, Thijs Dhollander, Maximilian Pietsch, Daan Christiaens, Ben Jeurissen, Chun-Hung Yeh, and Alan Connelly · 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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Adversarial neuron pruning purifies backdoored deep models
Dongxian Wu and Yisen Wang · 2021
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Rethinking the backdoor attacks’ triggers: A frequency perspective
Yi Zeng, Won Park, Z Morley Mao, and Ruoxi Jia · 2021
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Effective backdoor defense by exploiting sensitivity of poisoned samples
Weixin Chen, Baoyuan Wu, and Haoqian Wang · 2022
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Imperceptible and robust backdoor attack in 3d point cloud
Kuofeng Gao, Jiawang Bai, Baoyuan Wu, Mengxi Ya, and Shu-Tao Xia · 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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Efficient generalization improvement guided by random weight perturbation
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Regularized training and tight certification for randomized smoothed classifier with provable robustness
Huijie Feng, Chunpeng Wu, Guoyang Chen, Weifeng Zhang, and Yang Ning · 2020
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Backdoor attacks and countermeasures on deep learning: A comprehensive review
Yansong Gao, Bao Gia Doan, Zhi Zhang, Siqi Ma, Jiliang Zhang, Anmin Fu, Surya Nepal, and Hyoungshick Kim · 2020
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Liangkai Liu, Sidi Lu, Ren Zhong, Baofu Wu, Yongtao Yao, Qingyang Zhang, and Weisong Shi · 2020
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Input-aware dynamic backdoor attack
Tuan Anh Nguyen and Anh Tran · 2020
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Sharpness-aware minimization for efficiently improving generalization
Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur · 2021
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Asam: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks
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Tao Li, Weihao Yan, Zehao Lei, Yingwen Wu, Kun Fang, Ming Yang, and Xiaolin Huang · 2022
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Make sharpness-aware minimization stronger: A sparsified perturbation approach
Peng Mi, Li Shen, Tianhe Ren, Yiyi Zhou, Xiaoshuai Sun, Rongrong Ji, and Dacheng Tao · 2022
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Backdoorbench: A comprehensive benchmark of backdoor learning
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Robust weight perturbation for adversarial training
Chaojian Yu, Bo Han, Mingming Gong, Li Shen, Shiming Ge, Bo Du, and Tongliang Liu · 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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Data-free backdoor removal based on channel lipschitzness
Runkai Zheng, Rongjun Tang, Jianze Li, and Li Liu · 2022
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Pre-activation distributions expose backdoor neurons
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Improving sharpness-aware minimization with fisher mask for better generalization on language models
Qihuang Zhong, Liang Ding, Li Shen, Peng Mi, Juhua Liu, Bo Du, and Dacheng Tao · 2022
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Surrogate gap minimization improves sharpness-aware training
Juntang Zhuang, Boqing Gong, Liangzhe Yuan, Yin Cui, Hartwig Adam, Nicha C Dvornek, sekhar tatikonda, James s Duncan, and Ting Liu · 2022
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Robust generalization against corruptions via worst-case sharpness minimization, 2023
Zhuo Huang, Xiaobo Xia, Li Shen, Jun Yu, Chen Gong, Bo Han, and Tongliang Liu · 2023
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