Related papers: Robust Evaluation of Diffusion-Based Adversarial Purification

Robust Evaluation of Diffusion-Based Adversarial Purification

URL: http://arxiv.org/abs/2303.09051v3
Date: Sun, 3 Dec 2023 19:26:11 GMT
Title: Robust Evaluation of Diffusion-Based Adversarial Purification
Authors: Minjong Lee, Dongwoo Kim
Abstract summary: Diffusion-based purification methods aim to remove adversarial effects from an input data point at test time. White-box attacks are often employed to measure the robustness of the purification. We propose a new purification strategy improving robustness compared to the current diffusion-based purification methods.
Score: 3.634387981995277
License: http://creativecommons.org/licenses/by/4.0/
Abstract: We question the current evaluation practice on diffusion-based purification methods. Diffusion-based purification methods aim to remove adversarial effects from an input data point at test time. The approach gains increasing attention as an alternative to adversarial training due to the disentangling between training and testing. Well-known white-box attacks are often employed to measure the robustness of the purification. However, it is unknown whether these attacks are the most effective for the diffusion-based purification since the attacks are often tailored for adversarial training. We analyze the current practices and provide a new guideline for measuring the robustness of purification methods against adversarial attacks. Based on our analysis, we further propose a new purification strategy improving robustness compared to the current diffusion-based purification methods.

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