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ARBiBench: Benchmarking Adversarial Robustness of Binarized Neural Networks

arXiv 2312.13575 ↗First posted 21 Dec 2023cs.CV cs.LGcited by 0

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Abstract (arXiv)

Network binarization exhibits great potential for deployment on resource-constrained devices due to its low computational cost. Despite the critical importance, the security of binarized neural networks (BNNs) is rarely investigated. In this paper, we present ARBiBench, a comprehensive benchmark to evaluate the robustness of BNNs against adversarial perturbations on CIFAR-10 and ImageNet. We first evaluate the robustness of seven influential BNNs on various white-box and black-box attacks. The results reveal that 1) The adversarial robustness of BNNs exhibits a completely opposite performance on the two datasets under white-box attacks. 2) BNNs consistently exhibit better adversarial robustness under black-box attacks. 3) Different BNNs exhibit certain similarities in their robustness performance. Then, we conduct experiments to analyze the adversarial robustness of BNNs based on these insights. Our research contributes to inspiring future research on enhancing the robustness of BNNs and advancing their application in real-world scenarios.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 68 of its 83 references that are in the map. First posted on arXiv 21 Dec 2023.

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References · 68

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