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arXiv · 1911.10364

Universal Adversarial Robustness of Texture and Shape-Biased Models

Kenneth T. Co·Luis Muñoz-González·Leslie Kanthan·Ben Glocker·Emil C. Lupu
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Abstract

Increasing shape-bias in deep neural networks has been shown to improve robustness to common corruptions and noise. In this paper we analyze the adversarial robustness of texture and shape-biased models to Universal Adversarial Perturbations (UAPs). We use UAPs to evaluate the robustness of DNN models with varying degrees of shape-based training. We find that shape-biased models do not markedly improve adversarial robustness, and we show that ensembles of texture and shape-biased models can improve universal adversarial robustness while maintaining strong performance.

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Kenneth T. Co, Luis Muñoz-González, Leslie Kanthan, Ben Glocker, Emil C. Lupu. 2019-11-23. Universal Adversarial Robustness of Texture and Shape-Biased Models. https://doi.org/10.1109/icip42928.2021.9506325

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