Multi-scale Diffusion Denoised Smoothing

• URL: http://arxiv.org/abs/2310.16779v3
• Date: Fri, 27 Oct 2023 17:51:17 GMT
• Title: Multi-scale Diffusion Denoised Smoothing
• Authors: Jongheon Jeong, Jinwoo Shin
• Abstract summary: randomized smoothing has become one of a few tangible approaches that offers adversarial robustness to models at scale. We present scalable methods to address the current trade-off between certified robustness and accuracy in denoised smoothing. Our experiments show that the proposed multi-scale smoothing scheme combined with diffusion fine-tuning enables strong certified robustness available with high noise level.
• Score: 79.95360025953931
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Along with recent diffusion models, randomized smoothing has become one of a few tangible approaches that offers adversarial robustness to models at scale, e.g., those of large pre-trained models. Specifically, one can perform randomized smoothing on any classifier via a simple "denoise-and-classify" pipeline, so-called denoised smoothing, given that an accurate denoiser is available - such as diffusion model. In this paper, we present scalable methods to address the current trade-off between certified robustness and accuracy in denoised smoothing. Our key idea is to "selectively" apply smoothing among multiple noise scales, coined multi-scale smoothing, which can be efficiently implemented with a single diffusion model. This approach also suggests a new objective to compare the collective robustness of multi-scale smoothed classifiers, and questions which representation of diffusion model would maximize the objective. To address this, we propose to further fine-tune diffusion model (a) to perform consistent denoising whenever the original image is recoverable, but (b) to generate rather diverse outputs otherwise. Our experiments show that the proposed multi-scale smoothing scheme combined with diffusion fine-tuning enables strong certified robustness available with high noise level while maintaining its accuracy close to non-smoothed classifiers.

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