A Dual-Purpose Framework for Backdoor Defense and Backdoor Amplification in Diffusion Models

Diffusion models (AI systems that generate images and other content by gradually removing noise from random data) are vulnerable to backdoor attacks, where hidden triggers cause the model to produce harmful outputs. Researchers created PureDiffusion, a framework that can both defend against these attacks by detecting and inverting the hidden triggers, and amplify attacks by making existing backdoors more effective.