Identification of Attack-Specific Signatures in Adversarial Examples

TL;DR

This paper shows that different adversarial attack algorithms produce distinct adversarial examples, affecting neural networks in unique ways, and proposes methods to identify and analyze these differences.

Contribution

It introduces techniques to distinguish attack algorithms based on their effects and visualizations, highlighting the importance of considering downstream impacts.

Findings

Attack algorithms produce distinguishable adversarial examples

Different attacks target different network components and image regions

Deeper analysis can reveal the specific attack used

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.