<ul><li>Diffusion Denoised Smoothing is being explored as a technique to enhance model robustness against adversarial inputs.</li><li>Research is focusing on evaluating the effectiveness of Diffusion Denoised Smoothing beyond classification tasks.</li><li>Findings indicate that high-noise diffusion denoising significantly degrades model performance, while low-noise settings do not provide adequate protection against adversarial attacks.</li><li>A novel attack strategy targeting the diffusion process itself has been introduced, highlighting the challenge of balancing adversarial robustness and performance.</li></ul>

Beyond Classification: Evaluating Diffusion Denoised Smoothing for Security-Utility Trade off

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