# Diffusion or Non-Diffusion Adversarial Defenses: Rethinking the Relation between Classifier and Adversarial Purifier Source: [Diffusion or Non-Diffusion Adversarial Defenses: Rethinking the Relation between Classifier and Adversarial Purifier]() Collection index: [llms.txt]() Data updated: 2026-02-28T05:00:04.461Z Authors: Yuan-Chih Chen, Chun-Shien Lu Published: 2025-01-28T12:44:27.000Z ## Abstract Adversarial defense research continues to face challenges in combating against advanced adversarial attacks, yet with diffusion models increasingly favoring their defensive capabilities. Unlike most prior studies that focus on diffusion models for test-time defense, we explore the generalization loss in classifiers caused by diffusion models. We compare diffusion-based and non-diffusion-based adversarial purifiers, demonstrating that non-diffusion models can also achieve well performance under a practical setting of non-adaptive attack. While non-diffusion models show promising adversarial robustness, they particularly excel in defense transferability and color generalization without relying on additional data beyond the training set. Notably, a non-diffusion model trained on CIFAR-10 achieves state-of-the-art performance when tested directly on ImageNet, surpassing existing diffusion-based models trained specifically on ImageNet. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()