MimicDiffusion: Purifying Adversarial Perturbation via Mimicking Clean Diffusion Model

Deep neural networks (DNNs) are vulnerable to adversarial perturbation, where an imperceptible perturbation is added to the image that can fool the DNNs. Diffusion-based adversarial purification uses the diffusion model to generate a clean image against such adversarial attacks. Un-fortunately, the generative process of the diffusion model is also inevitably affected by adversarial perturbation since the diffusion model is also a deep neural network where its input has adversarial perturbation. In this work, we propose MimicDiffusion, a new diffusion-based adversarial purification technique that directly approximates the generative process of the diffusion model with the clean image as input. Concretely, we analyze the differences between the guided terms using the clean image and the ad-versarial sample. After that, we first implement MimicD-iffusion based on Manhattan distance. Then, we propose two guidance to purify the adversarial perturbation and ap-proximate the clean diffusion model. Extensive experiments on three image datasets, including CIFAR-10, CIFAR-100, and ImageNet, with three classifier backbones including WideResNet-70-16, WideResNet-28-10, and ResNet-50 demonstrate that MimicDiffusion significantly performs better than the state-of-the-art baselines. On CIFAR -10, CIFAR-100, and ImageNet, it achieves 92.67%, 61.35%, and 61.53% average robust accuracy, which are 18.49%, 13.23%, and 17.64% higher, respectively. The code is available at https://github.com/psky1111/MimicDiffusion.