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arXiv · 2307.16331

Theoretically Principled Trade-off for Stateful Defenses against Query-Based Black-Box Attacks

Ashish Hooda·Neal Mangaokar·Ryan Feng·Kassem Fawaz·Somesh Jha·Atul Prakash
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Abstract

Adversarial examples threaten the integrity of machine learning systems with alarming success rates even under constrained black-box conditions. Stateful defenses have emerged as an effective countermeasure, detecting potential attacks by maintaining a buffer of recent queries and detecting new queries that are too similar. However, these defenses fundamentally pose a trade-off between attack detection and false positive rates, and this trade-off is typically optimized by hand-picking feature extractors and similarity thresholds that empirically work well. There is little current understanding as to the formal limits of this trade-off and the exact properties of the feature extractors/underlying problem domain that influence it. This work aims to address this gap by offering a theoretical characterization of the trade-off between detection and false positive rates for stateful defenses. We provide upper bounds for detection rates of a general class of feature extractors and analyze the impact of this trade-off on the convergence of black-box attacks. We then support our theoretical findings with empirical evaluations across multiple datasets and stateful defenses.

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Ashish Hooda, Neal Mangaokar, Ryan Feng, Kassem Fawaz, Somesh Jha, Atul Prakash. 2023-07-30. Theoretically Principled Trade-off for Stateful Defenses against Query-Based Black-Box Attacks. https://arxiv.org/abs/2307.16331

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