Universal Distributional Decision-based Black-box Adversarial Attack with Reinforcement Learning

TL;DR

This paper introduces DBAR, a reinforcement learning-based decision-only black-box adversarial attack that improves success rates and transferability over existing methods, highlighting new strategies for model vulnerability assessment.

Contribution

The paper presents a novel reinforcement learning approach for decision-based black-box attacks, outperforming existing gradient-estimation methods in success rate and transferability.

Findings

Outperforms state-of-the-art decision-based attacks

Achieves higher attack success rate

Demonstrates greater transferability

Abstract

The vulnerability of the high-performance machine learning models implies a security risk in applications with real-world consequences. Research on adversarial attacks is beneficial in guiding the development of machine learning models on the one hand and finding targeted defenses on the other. However, most of the adversarial attacks today leverage the gradient or logit information from the models to generate adversarial perturbation. Works in the more realistic domain: decision-based attacks, which generate adversarial perturbation solely based on observing the output label of the targeted model, are still relatively rare and mostly use gradient-estimation strategies. In this work, we propose a pixel-wise decision-based attack algorithm that finds a distribution of adversarial perturbation through a reinforcement learning algorithm. We call this method Decision-based Black-box Attack with Reinforcement learning (DBAR). Experiments show that the proposed approach outperforms state-of-the-art decision-based attacks with a higher attack success rate and greater transferability.