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Check out our latest research on adversarial robustness and generalization of deep networks.
08thAPRIL2019

READING

Mahmood Sharif, Lujo Bauer, Michael K. Reiter. On the Suitability of Lp-Norms for Creating and Preventing Adversarial Examples. CVPR Workshops, 2018.

Sharif et al. study the effectiveness of $L_p$ norms for creating adversarial perturbations. In this context, their main discussion revolves around whether $L_p$ norms are sufficient and/or necessary for perceptual similarity. Their main conclusion is that $L_p$ norms are neither necessary nor sufficient to ensure perceptual similarity. For example, an adversarial example might be within a specific $L_p$ bal, but humans might still identify it as not similar enough to the originally attacked sample; on the other hand, there are also some imperceptible perturbations that usually extend beyond a reasonable $L_p$ ball. Such transformatons might for example include small rotations or translation. These findings are interesting because it indicates that our current model, or approximation, or perceptual similarity is not meaningful in all cases.

Also find this summary on ShortScience.org.

What is your opinion on the summarized work? Or do you know related work that is of interest? Let me know your thoughts in the comments below: