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Diffusion models (DM) have become state-of-the-art generative models becauseof their capability to generate high-quality images from noises withoutadversarial training. However, they are vulnerable to backdoor attacks asreported by recent studies. When a data input (e.g., some Gaussian noise) isstamped with a trigger (e.g., a white patch), the backdoored model alwaysgenerates the target image (e.g., an improper photo). However, effectivedefense strategies to mitigate backdoors from DMs are underexplored. To bridgethis gap, we propose the first backdoor detection and removal framework forDMs. We evaluate our framework Elijah on hundreds of DMs of 3 types includingDDPM, NCSN and LDM, with 13 samplers against 3 existing backdoor attacks.Extensive experiments show that our approach can have close to 100% detectionaccuracy and reduce the backdoor effects to close to zero without significantlysacrificing the model utility.

Elijah: Eliminating Backdoors Injected in Diffusion Models via Distribution Shift