Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks

TL;DR

This paper investigates why adversarial attacks transfer between models, identifying key factors like model vulnerability and surrogate complexity, supported by theoretical analysis and extensive experiments across various classifiers.

Contribution

It introduces a unifying framework and formal definitions for attack transferability, revealing the main factors influencing transfer success.

Findings

Transferability depends on model vulnerability and surrogate complexity.

Theoretical insights are validated with experiments on diverse classifiers.

Three metrics are identified as impacting attack transferability.

Abstract

Transferability captures the ability of an attack against a machine-learning model to be effective against a different, potentially unknown, model. Empirical evidence for transferability has been shown in previous work, but the underlying reasons why an attack transfers or not are not yet well understood. In this paper, we present a comprehensive analysis aimed to investigate the transferability of both test-time evasion and training-time poisoning attacks. We provide a unifying optimization framework for evasion and poisoning attacks, and a formal definition of transferability of such attacks. We highlight two main factors contributing to attack transferability: the intrinsic adversarial vulnerability of the target model, and the complexity of the surrogate model used to optimize the attack. Based on these insights, we define three metrics that impact an attack's transferability. Interestingly, our results derived from theoretical analysis hold for both evasion and poisoning attacks, and are confirmed experimentally using a wide range of linear and non-linear classifiers and datasets.