Identification of Attack-Specific Signatures in Adversarial Examples

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 pr...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-10
Main Authors: Souri, Hossein, Khorramshahi, Pirazh, Chun Pong Lau, Goldblum, Micah, Chellappa, Rama
Format: Article
Language:English
Subjects:
Algorithms
Downstream effects
Neural networks
Perturbation
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422