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arXiv · 2311.11191

Attention-Based Real-Time Defenses for Physical Adversarial Attacks in Vision Applications

Giulio Rossolini·Alessandro Biondi·Giorgio Buttazzo
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Abstract

Deep neural networks exhibit excellent performance in computer vision tasks, but their vulnerability to real-world adversarial attacks, achieved through physical objects that can corrupt their predictions, raises serious security concerns for their application in safety-critical domains. Existing defense methods focus on single-frame analysis and are characterized by high computational costs that limit their applicability in multi-frame scenarios, where real-time decisions are crucial. To address this problem, this paper proposes an efficient attention-based defense mechanism that exploits adversarial channel-attention to quickly identify and track malicious objects in shallow network layers and mask their adversarial effects in a multi-frame setting. This work advances the state of the art by enhancing existing over-activation techniques for real-world adversarial attacks to make them usable in real-time applications. It also introduces an efficient multi-frame defense framework, validating its efficacy through extensive experiments aimed at evaluating both defense performance and computational cost.

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Giulio Rossolini, Alessandro Biondi, Giorgio Buttazzo. 2023-11-19. Attention-Based Real-Time Defenses for Physical Adversarial Attacks in Vision Applications. https://doi.org/10.1109/iccps61052.2024.00009

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