FlowPure: Continuous Normalizing Flows for Adversarial Purification
This addresses the critical challenge of adversarial attacks for machine learning systems, offering an incremental improvement over existing purification methods.
The paper tackled adversarial robustness by proposing FlowPure, a purification method using Continuous Normalizing Flows to map adversarial examples to clean samples, which outperformed state-of-the-art defenses on CIFAR-10 and CIFAR-100 while preserving benign accuracy in preprocessor-blind scenarios.
Despite significant advancements in the area, adversarial robustness remains a critical challenge in systems employing machine learning models. The removal of adversarial perturbations at inference time, known as adversarial purification, has emerged as a promising defense strategy. To achieve this, state-of-the-art methods leverage diffusion models that inject Gaussian noise during a forward process to dilute adversarial perturbations, followed by a denoising step to restore clean samples before classification. In this work, we propose FlowPure, a novel purification method based on Continuous Normalizing Flows (CNFs) trained with Conditional Flow Matching (CFM) to learn mappings from adversarial examples to their clean counterparts. Unlike prior diffusion-based approaches that rely on fixed noise processes, FlowPure can leverage specific attack knowledge to improve robustness under known threats, while also supporting a more general stochastic variant trained on Gaussian perturbations for settings where such knowledge is unavailable. Experiments on CIFAR-10 and CIFAR-100 demonstrate that our method outperforms state-of-the-art purification-based defenses in preprocessor-blind and white-box scenarios, and can do so while fully preserving benign accuracy in the former. Moreover, our results show that not only is FlowPure a highly effective purifier but it also holds a strong potential for adversarial detection, identifying preprocessor-blind PGD samples with near-perfect accuracy.