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We propose functional adversarial attacks, a novel class of threat models for crafting adversarial examples to fool machine learning models.
Quantifying Perceptual Distortion of Adversarial Examples
Matt Jordan, Naren Manoj, Surbhi Goel, and Alexandros G. Dimakis · 1902
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Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
Eric Wong, Frank R. Schmidt, and J. Zico Kolter · 1902
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Field Guide to Visual and Ophthalmic Optics
Jim Schwiegerling · 2004
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Learning Multiple Layers of Features from Tiny Images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 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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Explaining and Harnessing Adversarial Examples
Ian Goodfellow, Jonathon Shlens, 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, and Michael Bernstein · 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
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Wide Residual Networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini and David Wagner · 2017
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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 · 2017
Cited alongside, same era.
On the Limitation of Convolutional Neural Networks in Recognizing Negative Images
Hossein Hosseini, Baicen Xiao, Mayoore Jaiswal, and Radha Poovendran · 2017
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Automatic Differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Constructing Unrestricted Adversarial Examples with Generative Models
Yang Song, Rui Shu, Nate Kushman, and Stefano Ermon · 2018
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Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
Jonathan Uesato, Brendan O’Donoghue, Pushmeet Kohli, and Aaron Oord · 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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The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang · 2018
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Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A. Alemi · 2017
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.
Semantic Adversarial Examples
Hossein Hosseini and Radha Poovendran · 2018
Cited alongside, same era.
Proper ResNet Implementation for CIFAR10/CIFAR100 in pytorch, 2018
Yerlan Idelbayev · 2018
Cited alongside, same era.
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow
Cited in the paper.
The Limitations of Deep Learning in Adversarial Settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami
Cited in the paper.
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami
Cited in the paper.
Big but Imperceptible Adversarial Perturbations via Semantic Manipulation
Anand Bhattad, Min Jin Chong, Kaizhao Liang, Bo Li, and David A. Forsyth · 2019
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Transfer of Adversarial Robustness Between Perturbation Types
Daniel Kang, Yi Sun, Tom Brown, Dan Hendrycks, and Jacob Steinhardt · 2019
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Adversarial Attacks Beyond the Image Space
Xiaohui Zeng, Chenxi Liu, Yu-Siang Wang, Weichao Qiu, Lingxi Xie, Yu-Wing Tai, Chi Keung Tang, and Alan L. Yuille · 2019
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Theoretically Principled Trade-off Between Robustness and Accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, and Michael I. Jordan · 2019
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