He YiMin

Universal Adversarial Purification with DDIM Metric Loss for Stable Diffusion

Stable Diffusion (SD) often produces degraded outputs when the training dataset contains adversarial noise. Adversarial purification offers a promising solution by removing adversarial noise from contaminated data. However, existing purification methods are primarily designed for classification task