From Deconstruction to Reconstruction: A Plug-In Module for Diffusion-Based Purification of Adversarial Examples

As the use and reliance on AI technologies continue to proliferate, there is mounting concern regarding adversarial example attacks, emphasizing the pressing necessity for robust defense strategies to protect AI systems from malicious input manipulation. In this paper, we introduce a computationally efficient plug-in module, seamlessly integrable with advanced diffusion models for purifying adversarial examples. Drawing inspiration from the concept of deconstruction and reconstruction (DR), our module decomposes an input image into foundational visual features expected to exhibit robustness against adversarial perturbations and subsequently rebuilds the image using an image-to-image transformation neural network. Through the collaborative integration of the module with an advanced diffusion model, this combination attains state-of-the-art performance in effectively purifying adversarial examples while preserving high classification accuracy on clean image samples. The model performance is evaluated on representative neural network classifiers pre-trained and fine-tuned on large-scale datasets. An ablation study analyses the impact of the proposed plug-in module on enhancing the effectiveness of diffusion-based purification. Furthermore, it is noteworthy that the module demonstrates significant computational efficiency, incurring only minimal computational overhead during the purification process.