# Instant Adversarial Purification with Adversarial Consistency Distillation Source: [Instant Adversarial Purification with Adversarial Consistency Distillation]() Collection index: [llms.txt]() Data updated: 2026-01-04T04:56:51.359Z Authors: Chun Tong Lei, Hon Ming Yam, Zhongliang Guo, Yifei Qian, Chun Pong Lau Published: 2024-08-30T07:49:35.000Z ## Abstract Neural networks have revolutionized numerous fields with their exceptional performance, yet they remain susceptible to adversarial attacks through subtle perturbations. While diffusion-based purification methods like DiffPure offer promising defense mechanisms, their computational overhead presents a significant practical limitation. In this paper, we introduce One Step Control Purification (OSCP), a novel defense framework that achieves robust adversarial purification in a single Neural Function Evaluation (NFE) within diffusion models. We propose Gaussian Adversarial Noise Distillation (GAND) as the distillation objective and Controlled Adversarial Purification (CAP) as the inference pipeline, which makes OSCP demonstrate remarkable efficiency while maintaining defense efficacy. Our proposed GAND addresses a fundamental tension between consistency distillation and adversarial perturbation, bridging the gap between natural and adversarial manifolds in the latent space, while remaining computationally efficient through Parameter-Efficient Fine-Tuning (PEFT) methods such as LoRA, eliminating the high computational budget request from full parameter fine-tuning. The CAP guides the purification process through the unlearnable edge detection operator calculated by the input image as an extra prompt, effectively preventing the purified images from deviating from their original appearance when large purification steps are used. Our experimental results on ImageNet showcase OSCP's superior performance, achieving a 74.19% defense success rate with merely 0.1s per purification -- a 100-fold speedup compared to conventional approaches. ## 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. - [Image Classification on ImageNet (Standard and Robust Accuracy)](): Image Classification; ImageNet; Standard Accuracy: 79.81. - [Image Classification on ImageNet 1k (test) (Robustness Metrics)](): Image Classification; ImageNet 1k (test); Clean Accuracy: 79.81. ## Original sources and implementation [Original paper]() [PDF]()