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Adversarial defenses via vector quantization
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AI
DOI：10.1016/j.neucom.2025.130703.png)
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Abstract
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Adversarial attacks pose significant challenges to the robustness of modern deep neural networks in computer vision, and defending these networks against adversarial attacks has attracted intense research efforts. Among various defense strategies, preprocessing-based defenses are practically appealing since there is no need to train the network under protection. However, such approaches typically do not achieve comparable robustness as other methods such as adversarial training. In this paper, we propose a novel framework for preprocessing-based defenses, where a vector quantizer is used as a preprocessor. This framework, inspired by and extended from Randomized Discretization (“RandDisc”), is theoretically principled by rate–distortion theory: indeed, RandDisc may be viewed as a scalar quantizer, and rate–distortion theory suggests that such quantization schemes are inferior to vector quantization. In our framework, the preprocessing vector quantizer treats the input image as a collection of patches and finds a set of “representative patches” based on the patch distributions; each original patch is then modified according to the representative patches close to it.
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6.5
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