How stealthy is stealthy? Studying the Efficacy of Black-Box Adversarial Attacks in the Real World

• URL: http://arxiv.org/abs/2506.05382v1
• Date: Tue, 03 Jun 2025 08:56:37 GMT
• Title: How stealthy is stealthy? Studying the Efficacy of Black-Box Adversarial Attacks in the Real World
• Authors: Francesco Panebianco, Mario D'Onghia, Stefano Zanero aand Michele Carminati,
• Abstract summary: This study investigates black-box adversarial attacks in computer vision.<n>We propose ECLIPSE, a novel attack method employing Gaussian blurring on sampled gradients and a local surrogate model.
• Score: 1.799933345199395
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning systems, critical in domains like autonomous vehicles, are vulnerable to adversarial examples (crafted inputs designed to mislead classifiers). This study investigates black-box adversarial attacks in computer vision. This is a realistic scenario, where attackers have query-only access to the target model. Three properties are introduced to evaluate attack feasibility: robustness to compression, stealthiness to automatic detection, and stealthiness to human inspection. State-of-the-Art methods tend to prioritize one criterion at the expense of others. We propose ECLIPSE, a novel attack method employing Gaussian blurring on sampled gradients and a local surrogate model. Comprehensive experiments on a public dataset highlight ECLIPSE's advantages, demonstrating its contribution to the trade-off between the three properties.

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