Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on mathbbso(d)
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Abstract (arXiv)
We show that adversarial examples exist for various random convolutional networks, and furthermore, that this is a relatively simple consequence of the isoperimetric inequality on the special orthogonal group mathbbso(d). This extends and simplifies a recent line of work which shows similar results for random fully connected networks.
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References · 23
- 2014Explaining and Harnessing Adversarial Examplescited by 7,287
- 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
- 2013Intriguing properties of neural networkscited by 5,114
- 2018Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examplescited by 1,259
- 2016Practical Black-Box Attacks against Machine Learningcited by 893
- 2015Distillation as a Defense to Adversarial Perturbations against Deep Neural Networkscited by 675
- 2017Provable defenses against adversarial examples via the convex outer adversarial polytopecited by 564
- 2017Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methodscited by 542
- 2018Adversarially Robust Generalization Requires More Datacited by 318
- 2018Audio Adversarial Examples: Targeted Attacks on Speech-to-Textcited by 268
- 2017Detecting Adversarial Samples from Artifactscited by 244
- 2017On the (Statistical) Detection of Adversarial Examplescited by 180
11 more
- 2016Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivitycited by 138
- 2018Are adversarial examples inevitable?cited by 113
- 2018Adversarial examples from computational constraintscited by 79
- 2018Adversarial vulnerability for any classifiercited by 78
- 2018The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measurecited by 44
- 2019A Simple Explanation for the Existence of Adversarial Examples with Small Hamming Distancecited by 32
- 2022Gradient Methods Provably Converge to Non-Robust Networkscited by 27
- 2021A single gradient step finds adversarial examples on random two-layers neural networkscited by 26
- 2021Adversarial Examples in Multi-Layer Random ReLU Networkscited by 26
- 2022Adversarial Examples in Random Neural Networks with General Activationscited by 13
- 2023Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Linear Subspacescited by 8
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