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Workshop

Attacking the Madry Defense Model with L1-based Adversarial Examples

Yash Sharma · Pin-Yu Chen

East Meeting Level 8 + 15 #15

Tue 1 May, 4:30 p.m. PDT

The Madry Lab recently hosted a competition designed to test the robustness of their adversarially trained MNIST model. Attacks were constrained to perturb each pixel of the input image by a scaled maximal L distortion ϵ = 0.3. This decision discourages the use of attacks which are not optimized on the L distortion metric. Our experimental results demonstrate that by relaxing the L constraint of the competition, the \textbf{e}lastic-net \textbf{a}ttack to \textbf{d}eep neural networks (EAD) can generate transferable adversarial examples which, despite their high average L distortion, have minimal visual distortion. These results call into question the use of L as a sole measure for visual distortion, and further demonstrate the power of EAD at generating robust adversarial examples.

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