RED – ROBUST ENVIRONMENTAL DESIGN
Keywords: adversarial machine learning; security
Abstract: The classification of road signs by autonomous systems, especially those reliant on
visual inputs, is highly susceptible to adversarial attacks. Traditional approaches to
mitigating such vulnerabilities have focused on enhancing the robustness of classi-
fication models. In contrast, this paper adopts a fundamentally different strategy
aimed at increasing robustness through the redesign of road signs themselves. We
propose an attacker-agnostic learning scheme to automatically design road signs
that are robust to a wide array of patch-based attacks. Empirical tests conducted in
both digital and physical environments demonstrate that our approach significantly
reduces vulnerability to patch attacks, outperforming existing techniques.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 12579
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