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Previous work shows that adversarially robust generalization requires larger sample complexity, and the same dataset, e.g., CIFAR-10, which enables good standard accuracy may not suffice to train robust models.
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Evasion attacks against machine learning at test time
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Deep learning face representation by joint identification-verification
Yi Sun, Yuheng Chen, Xiaogang Wang, and Xiaoou Tang · 2014
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
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Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
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Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, and Pascal Frossard · 2016
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Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Large-margin softmax loss for convolutional neural networks
Weiyang Liu, Yandong Wen, Zhiding Yu, and Meng Yang · 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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Guaranteed bounds on the kullback–leibler divergence of univariate mixtures
Frank Nielsen and Ke Sun · 2016
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The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami · 2016
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A discriminative feature learning approach for deep face recognition
Yandong Wen, Kaipeng Zhang, Zhifeng Li, and Yu Qiao · 2016
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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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One-shot face recognition by promoting underrepresented classes
Yandong Guo and Lei Zhang · 2017
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Formal guarantees on the robustness of a classifier against adversarial manipulation
Matthias Hein and Maksym Andriushchenko · 2017
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Face recognition via centralized coordinate learning
Xianbiao Qi and Lei Zhang · 2018
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Adversarially robust generalization requires more data
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, and Aleksander Madry · 2018
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Adversarial risk and the dangers of evaluating against weak attacks
Jonathan Uesato, Brendan O’Donoghue, Aaron van den Oord, and Pushmeet Kohli · 2018
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Rethinking feature distribution for loss functions in image classification
Weitao Wan, Yuanyi Zhong, Tianpeng Li, and Jiansheng Chen · 2018
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Cosface: Large margin cosine loss for deep face recognition
Hao Wang, Yitong Wang, Zheng Zhou, Xing Ji, Dihong Gong, Jingchao Zhou, Zhifeng Li, and Wei Liu · 2018
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Sok: Science, security and the elusive goal of security as a scientific pursuit
Cormac Herley and Paul C Van Oorschot · 2017
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Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2017
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Soft-margin softmax for deep classification
Xuezhi Liang, Xiaobo Wang, Zhen Lei, Shengcai Liao, and Stan Z Li · 2017
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Sphereface: Deep hypersphere embedding for face recognition
Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj, and Le Song · 2017
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Normface: l 2 hypersphere embedding for face verification
Feng Wang, Xiang Xiang, Jian Cheng, and Alan Loddon Yuille · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
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Boosting adversarial attacks with momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and Zico Kolter · 2018
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Scaling provable adversarial defenses
Eric Wong, Frank Schmidt, Jan Hendrik Metzen, and J Zico Kolter · 2018
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On evaluating adversarial robustness
Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian Goodfellow, Aleksander Madry, and Alexey Kurakin · 2019
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Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, and John C Duchi · 2019
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Arcface: Additive angular margin loss for deep face recognition
Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou · 2019
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Uniformface: Learning deep equidistributed representation for face recognition
Yueqi Duan, Jiwen Lu, and Jie Zhou · 2019
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A rotation and a translation suffice: Fooling cnns with simple transformations
Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, and Aleksander Madry · 2019
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Adversarial examples are a natural consequence of test error in noise
Justin Gilmer, Nicolas Ford, Nicolas Carlini, and Ekin Cubuk · 2019
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Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Anish Athalye, Tran, and Aleksander Madry · 2019
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Adversarial defense by restricting the hidden space of deep neural networks
Aamir Mustafa, Salman Khan, Munawar Hayat, Roland Goecke, Jianging Shen, and Ling Shao · 2019
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Improving adversarial robustness via promoting ensemble diversity
Tianyu Pang, Kun Xu, Chao Du, Ning Chen, and Jun Zhu · 2019
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Adversarial training for free!
Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein · 2019
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Are labels required for improving adversarial robustness?
Robert Stanforth, Alhussein Fawzi, Pushmeet Kohli, et al · 2019
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Training for faster adversarial robustness verification via inducing relu stability
Kai Y Xiao, Vincent Tjeng, Nur Muhammad Shafiullah, and Aleksander Madry · 2019
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Feature denoising for improving adversarial robustness
Cihang Xie, Yuxin Wu, Laurens van der Maaten, Alan Yuille, and Kaiming He · 2019
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