On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attacks

TL;DR

This paper investigates whether training with unrealistic adversarial examples enhances model robustness against realistic attacks across various real-world scenarios, revealing mixed effectiveness and analyzing underlying representation patterns.

Contribution

It provides an empirical study on the effectiveness of unrealistic adversarial hardening against realistic attacks in multiple use cases and analyzes the representation differences to explain the results.

Findings

Unrealistic adversarial examples can sometimes match the effectiveness of realistic ones.

Effectiveness of unrealistic examples varies across different use cases.

Analysis of latent representations explains when unrealistic examples are beneficial.

Abstract

While the literature on security attacks and defense of Machine Learning (ML) systems mostly focuses on unrealistic adversarial examples, recent research has raised concern about the under-explored field of realistic adversarial attacks and their implications on the robustness of real-world systems. Our paper paves the way for a better understanding of adversarial robustness against realistic attacks and makes two major contributions. First, we conduct a study on three real-world use cases (text classification, botnet detection, malware detection)) and five datasets in order to evaluate whether unrealistic adversarial examples can be used to protect models against realistic examples. Our results reveal discrepancies across the use cases, where unrealistic examples can either be as effective as the realistic ones or may offer only limited improvement. Second, to explain these results, we analyze the latent representation of the adversarial examples generated with realistic and unrealistic attacks. We shed light on the patterns that discriminate which unrealistic examples can be used for effective hardening. We release our code, datasets and models to support future research in exploring how to reduce the gap between unrealistic and realistic adversarial attacks.