Adversarial Token Attacks on Vision Transformers

Ameya Joshi; Gauri Jagatap; Chinmay Hegde

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arXiv · 2110.04337

Adversarial Token Attacks on Vision Transformers

Ameya Joshi·Gauri Jagatap·Chinmay Hegde

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Abstract

Vision transformers rely on a patch token based self attention mechanism, in contrast to convolutional networks. We investigate fundamental differences between these two families of models, by designing a block sparsity based adversarial token attack. We probe and analyze transformer as well as convolutional models with token attacks of varying patch sizes. We infer that transformer models are more sensitive to token attacks than convolutional models, with ResNets outperforming Transformer models by up to $\sim30\%$ in robust accuracy for single token attacks.

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Ameya Joshi, Gauri Jagatap, Chinmay Hegde. 2021-10-08. Adversarial Token Attacks on Vision Transformers. https://arxiv.org/abs/2110.04337

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