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The vulnerability of deep neural networks (DNNs) to adversarial examples has drawn great attention from the community.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Earlier work this paper cites.
Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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DeepFool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2017
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Delving into transferable adversarial examples and black-box attacks
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song · 2017
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Adversarial machine learning at scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2017
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Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh · 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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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 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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Deep pyramidal residual networks
Dongyoon Han, Jiwhan Kim, and Junmo Kim · 2017
Earlier work this paper cites.
Delving into transferable adversarial examples and black-box attacks
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song · 2017
Cited alongside, same era.
Intriguing properties of adversarial examples
Ekin D Cubuk, Barret Zoph, Samuel S Schoenholz, and Quoc V Le · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
Ead: elastic-net attacks to deep neural networks via adversarial examples
Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, and Cho-Jui Hsieh · 2018
Cited alongside, same era.
Boosting adversarial attacks with momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li · 2018
Cited alongside, same era.
Black-box adversarial attacks with limited queries and information
Andrew Ilyas, Logan Engstrom, Anish Athalye, and Jessy Lin · 2018
Subspace attack: Exploiting promising subspaces for query-efficient black-box attacks
Ziang Yan, Yiwen Guo, and Changshui Zhang · 2019
Later among the works it cites.
Prior convictions: Black-box adversarial attacks with bandits and priors
Andrew Ilyas, Logan Engstrom, and Aleksander Madry · 2019
Later among the works it cites.
Autozoom: Autoencoder-based zeroth order optimization method for attacking black-box neural networks
Chun-Chen Tu, Paishun Ting, Pin-Yu Chen, Sijia Liu, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh, and Shin-Ming Cheng · 2019
Later among the works it cites.
Simple black-box adversarial attacks
Chuan Guo, Jacob R Gardner, Yurong You, Andrew Gordon Wilson, and Kilian Q Weinberger · 2019
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Hopskipjumpattack: A query-efficient decision-based attack
Jianbo Chen, Michael I Jordan, and Martin J Wainwright · 2019
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Cited alongside, same era.
Practical black-box attacks on deep neural networks using efficient query mechanisms
Arjun Nitin Bhagoji, Warren He, Bo Li, and Dawn Song · 2018
Cited alongside, same era.
Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Wieland Brendel, Jonas Rauber, and Matthias Bethge · 2018
Cited alongside, same era.
Transferable adversarial perturbations
Wen Zhou, Xin Hou, Yongjun Chen, Mengyun Tang, Xiangqi Huang, Xiang Gan, and Yong Yang · 2018
Cited alongside, same era.
Sparse dnns with improved adversarial robustness
Yiwen Guo, Chao Zhang, Changshui Zhang, and Yurong Chen · 2018
Cited alongside, same era.
Shakedrop regularization for deep residual learning
Yoshihiro Yamada, Masakazu Iwamura, Takuya Akiba, and Koichi Kise · 2018
Cited alongside, same era.
Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Cited alongside, same era.
Later among the works it cites.
Feature space perturbations yield more transferable adversarial examples
Nathan Inkawhich, Wei Wen, Hai Helen Li, and Yiran Chen · 2019
Later among the works it cites.
Enhancing adversarial example transferability with an intermediate level attack
Qian Huang, Isay Katsman, Horace He, Zeqi Gu, Serge Belongie, and Ser-Nam Lim · 2019
Later among the works it cites.
Improving transferability of adversarial examples with input diversity
Cihang Xie, Zhishuai Zhang, Yuyin Zhou, Song Bai, Jianyu Wang, Zhou Ren, and Alan L Yuille · 2019
Later among the works it cites.
Demystifying dropout
Hongchang Gao, Jian Pei, and Heng Huang · 2019
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Autoaugment: Learning augmentation policies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2019
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Searching for a robust neural architecture in four gpu hours
Xuanyi Dong and Yi Yang · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Later among the works it cites.
Feature space perturbations yield more transferable adversarial examples
Nathan Inkawhich, Wei Wen, Hai Helen Li, and Yiran Chen · 2019
Later among the works it cites.
Enhancing adversarial example transferability with an intermediate level attack
Qian Huang, Isay Katsman, Horace He, Zeqi Gu, Serge Belongie, and Ser-Nam Lim · 2019
Later among the works it cites.
Yet another intermediate-leve attack
Qizhang Li, Yiwen Guo, and Hao Chen · 2020
Closest in time.
Black-box adversarial attack with transferable model-based embedding
Zhichao Huang and Tong Zhang · 2020
Closest in time.
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
Closest in time.
On connections between regularizations for improving dnn robustness
Yiwen Guo, Long Chen, Yurong Chen, and Changshui Zhang · 2020
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Overfitting in adversarially robust deep learning
Leslie Rice, Eric Wong, and J Zico Kolter · 2020
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Yet another intermediate-leve attack
Qizhang Li, Yiwen Guo, and Hao Chen · 2020
Closest in time.
Practical no-box adversarial attacks against dnns
Qizhang Li, Yiwen Guo, and Hao Chen · 2020
Closest in time.