A Random Ensemble of Encrypted Vision Transformers for Adversarially Robust Defense

Ryota Iijima; Sayaka Shiota; Hitoshi Kiya

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arXiv · 2402.07183

A Random Ensemble of Encrypted Vision Transformers for Adversarially Robust Defense

Ryota Iijima·Sayaka Shiota·Hitoshi Kiya

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Abstract

Deep neural networks (DNNs) are well known to be vulnerable to adversarial examples (AEs). In previous studies, the use of models encrypted with a secret key was demonstrated to be robust against white-box attacks, but not against black-box ones. In this paper, we propose a novel method using the vision transformer (ViT) that is a random ensemble of encrypted models for enhancing robustness against both white-box and black-box attacks. In addition, a benchmark attack method, called AutoAttack, is applied to models to test adversarial robustness objectively. In experiments, the method was demonstrated to be robust against not only white-box attacks but also black-box ones in an image classification task on the CIFAR-10 and ImageNet datasets. The method was also compared with the state-of-the-art in a standardized benchmark for adversarial robustness, RobustBench, and it was verified to outperform conventional defenses in terms of clean accuracy and robust accuracy.

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BibTeX RIS

Ryota Iijima, Sayaka Shiota, Hitoshi Kiya. 2024-02-11. A Random Ensemble of Encrypted Vision Transformers for Adversarially Robust Defense. https://arxiv.org/abs/2402.07183

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