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DensePure: Understanding Diffusion Models towards Adversarial Robustness. (arXiv:2211.00322v1 [cs.LG])
Nov. 2, 2022, 1:24 a.m. | Chaowei Xiao, Zhongzhu Chen, Kun Jin, Jiongxiao Wang, Weili Nie, Mingyan Liu, Anima Anandkumar, Bo Li, Dawn Song
cs.CR updates on arXiv.org arxiv.org
Diffusion models have been recently employed to improve certified robustness
through the process of denoising. However, the theoretical understanding of why
diffusion models are able to improve the certified robustness is still lacking,
preventing from further improvement. In this study, we close this gap by
analyzing the fundamental properties of diffusion models and establishing the
conditions under which they can enhance certified robustness. This deeper
understanding allows us to propose a new method DensePure, designed to improve
the certified robustness …
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