Adversarial Diversity and Hard Positive Generation Adversarial Diversity and Hard Positive Generation
Paper summary Rozsa et al. propose PASS, an perceptual similarity metric invariant to homographies to quantify adversarial perturbations. In particular, PASS is based on the structural similarity metric SSIM [1]; specifically $PASS(\tilde{x}, x) = SSIM(\psi(\tilde{x},x), x)$ where $\psi(\tilde{x}, x)$ transforms the perturbed image $\tilde{x}$ to the image $x$ by applying a homography $H$ (which can be found through optimization). Based on this similarity metric, they consider additional attacks which create small perturbations in terms of the PASS score, but result in larger $L_p$ norms; see the paper for experimental results. [1] Z. Wang, A. C. Bovik, H. R. Sheikh, E. P. Simoncelli. Image quality assessment: from error visibility to structural similarity. TIP, 2004. Also see this summary at [davidstutz.de](https://davidstutz.de/category/reading/).
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Adversarial Diversity and Hard Positive Generation
Andras Rozsa and Ethan M. Rudd and Terrance E. Boult
arXiv e-Print archive - 2016 via Local arXiv
Keywords: cs.CV

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Summary by David Stutz 3 months ago
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