Related papers: Identification of Attack-Specific Signatures in Adversarial Examples

Identification of Attack-Specific Signatures in Adversarial Examples

URL: http://arxiv.org/abs/2110.06802v1
Date: Wed, 13 Oct 2021 15:40:48 GMT
Title: Identification of Attack-Specific Signatures in Adversarial Examples
Authors: Hossein Souri, Pirazh Khorramshahi, Chun Pong Lau, Micah Goldblum, Rama Chellappa
Abstract summary: We show that different attack algorithms produce adversarial examples which are distinct not only in their effectiveness but also in how they qualitatively affect their victims. Our findings suggest that prospective adversarial attacks should be compared not only via their success rates at fooling models but also via deeper downstream effects they have on victims.
Score: 62.17639067715379
License: http://creativecommons.org/licenses/by/4.0/
Abstract: The adversarial attack literature contains a myriad of algorithms for crafting perturbations which yield pathological behavior in neural networks. In many cases, multiple algorithms target the same tasks and even enforce the same constraints. In this work, we show that different attack algorithms produce adversarial examples which are distinct not only in their effectiveness but also in how they qualitatively affect their victims. We begin by demonstrating that one can determine the attack algorithm that crafted an adversarial example. Then, we leverage recent advances in parameter-space saliency maps to show, both visually and quantitatively, that adversarial attack algorithms differ in which parts of the network and image they target. Our findings suggest that prospective adversarial attacks should be compared not only via their success rates at fooling models but also via deeper downstream effects they have on victims.

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