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While the untargeted black-box transferability of adversarial perturbations has been extensively studied before, changing an unseen model's decisions to a specific `targeted' class remains a challenging feat.
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ImageNet Large Scale Visual Recognition Challenge
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Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
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Delving into transferable adversarial examples and black-box attacks
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song · 2016
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Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2017
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Fast feature fool: A data independent approach to universal adversarial perturbations
Konda Reddy Mopuri, Utsav Garg, and R Venkatesh Babu · 2017
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Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li · 2018
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Feature distillation: Dnn-oriented jpeg compression against adversarial examples
Zihao Liu, Qi Liu, Tao Liu, Yanzhi Wang, and Wujie Wen · 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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Generalizable data-free objective for crafting universal adversarial perturbations
Konda Reddy Mopuri, Aditya Ganeshan, and R Venkatesh Babu · 2018
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Generative adversarial perturbations
Omid Poursaeed, Isay Katsman, Bicheng Gao, and Serge Belongie · 2018
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Ask, acquire, and attack: Data-free uap generation using class impressions
Konda Reddy Mopuri, Phani Krishna Uppala, and R Venkatesh Babu · 2018
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Nag: Network for adversary generation
Konda Reddy Mopuri, Utkarsh Ojha, Utsav Garg, and R Venkatesh Babu · 2018
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Transferable adversarial perturbations
Wen Zhou, Xin Hou, Yongjun Chen, Mengyun Tang, Xiangqi Huang, Xiang Gan, and Yong Yang · 2018
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Evading defenses to transferable adversarial examples by translation-invariant attacks
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Backpropagating linearly improves transferability of adversarial examples
Yiwen Guo, Qizhang Li, and Hao Chen · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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AugMix: A simple data processing method to improve robustness and uncertainty
Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2020
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Transferable perturbations of deep feature distributions
Nathan Inkawhich, Kevin Liang, Lawrence Carin, and Yiran Chen · 2020
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Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu · 2019
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Adversarial examples are a natural consequence of test error in noise
Nic Ford, Justin Gilmer, Nicolas Carlini, and Dogus Cubuk · 2019
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Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel · 2019
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Enhancing adversarial example transferability with an intermediate level attack
Qian Huang, Isay Katsman, Horace He, Zeqi Gu, Serge Belongie, and Ser-Nam Lim · 2019
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Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
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Feature space perturbations yield more transferable adversarial examples
Nathan Inkawhich, Wei Wen, Hai Helen Li, and Yiran Chen · 2019
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Yingwei Li, Song Bai, Cihang Xie, Zhenyu Liao, Xiaohui Shen, and Alan L Yuille · 2019
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Nathan Inkawhich, Kevin J Liang, Binghui Wang, Matthew Inkawhich, Lawrence Carin, and Yiran Chen · 2020
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Towards transferable targeted attack
Maosen Li, Cheng Deng, Tengjiao Li, Junchi Yan, Xinbo Gao, and Heng Huang · 2020
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Yet another intermediate-level attack
Qizhang Li, Yiwen Guo, and Hao Chen · 2020
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A self-supervised approach for adversarial robustness
Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Fatih Porikli · 2020
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Do adversarially robust imagenet models transfer better?
Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, and Aleksander Madry · 2020
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Skip connections matter: On the transferability of adversarial examples generated with resnets
Dongxian Wu, Yisen Wang, Shu-Tao Xia, James Bailey, and Xingjun Ma · 2020
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Boosting the transferability of adversarial samples via attention
Weibin Wu, Yuxin Su, Xixian Chen, Shenglin Zhao, Irwin King, Michael R Lyu, and Yu-Wing Tai · 2020
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Transformers in vision: A survey
Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, and Mubarak Shah · 2021
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Intriguing properties of vision transformers
Muzammal Naseer, Kanchana Ranasinghe, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang · 2021
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On improving adversarial transferability of vision transformers
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