  We propose a novel algorithm, Salient Conditional Diffusion (Sancdifi), a
state-of-the-art defense against backdoor attacks. Sancdifi uses a denoising
diffusion probabilistic model (DDPM) to degrade an image with noise and then
recover said image using the learned reverse diffusion. Critically, we compute
saliency map-based masks to condition our diffusion, allowing for stronger
diffusion on the most salient pixels by the DDPM. As a result, Sancdifi is
highly effective at diffusing out triggers in data poisoned by backdoor
attacks. At the same time, it reliably recovers salient features when applied
to clean data. This performance is achieved without requiring access to the
model parameters of the Trojan network, meaning Sancdifi operates as a
black-box defense.
