Adaptive Diffusion Denoised Smoothing : Certified Robustness via Randomized Smoothing with Differentially Private Guided Denoising Diffusion
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912475889270784 |
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| author | Shpilevskiy, Frederick Lyu, Saiyue Dvijotham, Krishnamurthy Dj Lécuyer, Mathias Noël, Pierre-André |
| author_facet | Shpilevskiy, Frederick Lyu, Saiyue Dvijotham, Krishnamurthy Dj Lécuyer, Mathias Noël, Pierre-André |
| contents | We propose Adaptive Diffusion Denoised Smoothing, a method for certifying the predictions of a vision model against adversarial examples, while adapting to the input. Our key insight is to reinterpret a guided denoising diffusion model as a long sequence of adaptive Gaussian Differentially Private (GDP) mechanisms refining a pure noise sample into an image. We show that these adaptive mechanisms can be composed through a GDP privacy filter to analyze the end-to-end robustness of the guided denoising process, yielding a provable certification that extends the adaptive randomized smoothing analysis. We demonstrate that our design, under a specific guiding strategy, can improve both certified accuracy and standard accuracy on ImageNet for an $\ell_2$ threat model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08163 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Diffusion Denoised Smoothing : Certified Robustness via Randomized Smoothing with Differentially Private Guided Denoising Diffusion Shpilevskiy, Frederick Lyu, Saiyue Dvijotham, Krishnamurthy Dj Lécuyer, Mathias Noël, Pierre-André Computer Vision and Pattern Recognition Cryptography and Security Machine Learning We propose Adaptive Diffusion Denoised Smoothing, a method for certifying the predictions of a vision model against adversarial examples, while adapting to the input. Our key insight is to reinterpret a guided denoising diffusion model as a long sequence of adaptive Gaussian Differentially Private (GDP) mechanisms refining a pure noise sample into an image. We show that these adaptive mechanisms can be composed through a GDP privacy filter to analyze the end-to-end robustness of the guided denoising process, yielding a provable certification that extends the adaptive randomized smoothing analysis. We demonstrate that our design, under a specific guiding strategy, can improve both certified accuracy and standard accuracy on ImageNet for an $\ell_2$ threat model. |
| title | Adaptive Diffusion Denoised Smoothing : Certified Robustness via Randomized Smoothing with Differentially Private Guided Denoising Diffusion |
| topic | Computer Vision and Pattern Recognition Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2507.08163 |