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While deep neural networks have proven to be a powerful tool for many recognition and classification tasks, their stability properties are still not well understood.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Manitest: Are classifiers really invariant?
Alhussein Fawzi and Pascal Frossard · 2015
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Spatial transformer networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, and Koray Kavukcuoglu · 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
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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
Earlier work this paper cites.
Adversarial diversity and hard positive generation
Andras Rozsa, Ethan M Rudd, and Terrance E Boult · 2016
Earlier work this paper cites.
Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, and Michael K Reiter · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Cited alongside, same era.
Synthesizing robust adversarial examples
Anish Athalye and Ilya Sutskever · 2017
Cited alongside, same era.
Tom B Brown, Dandelion Mané, Aurko Roy, Martín Abadi, and Justin Gilmer · 2017
Cited alongside, same era.
A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations
Logan Engstrom, Dimitris Tsipras, Ludwig Schmidt, and Aleksander Madry · 2017
Threat of adversarial attacks on deep learning in computer vision: A survey
N. Akhtar and A. Mian · 2018
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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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Harini Kannan, Alexey Kurakin, and Ian Goodfellow · 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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Certified defenses against adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang · 2018
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Cited alongside, same era.
Adversarial perturbations and deformations for convolutional neural networks
Tandri Gauksson · 2017
Cited alongside, same era.
Universal adversarial perturbations
S. M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard · 2017
Cited alongside, same era.
Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2017
Cited alongside, same era.
Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David Wagner
Cited in the paper.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner
Cited in the paper.
Adversarial machine learning at scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio
Cited in the paper.
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio
Cited in the paper.
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 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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Spatially transformed adversarial examples
Chaowei Xiao, Jun-Yan Zhu, Bo Li, Warren He, Mingyan Liu, and Dawn Song · 2018
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