Diffusion Purification

Diffusion purification leverages diffusion models to remove noise, particularly adversarial perturbations, from data, aiming to improve the robustness of machine learning models against attacks. Current research focuses on enhancing the efficiency and effectiveness of these purification methods, exploring various architectures like low-rank iterative diffusion and one-step purification techniques, and addressing challenges such as computational cost and the preservation of semantic information. This field is significant for bolstering the security and reliability of AI systems across diverse applications, from image classification and natural language processing to chemical purification and 3D point cloud recognition, by mitigating the impact of adversarial examples and noisy data.