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Check out our latest research on weakly-supervised 3D shape completion.
20thAUGUST2018

READING

Andras Rozsa, Ethan M. Rudd, Terrance E. Boult. Adversarial Diversity and Hard Positive Generation. CVPR Workshops 2016: 410-417.

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 find this summary on ShortScience.org.

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