DADet: Safeguarding Image Conditional Diffusion Models against Adversarial and Backdoor Attacks via Diffusion Anomaly Detection

While image conditional diffusion models demonstrate impressive generation capabilities, they exhibit high vulnerability when facing backdoor and adversarial attacks. In this paper, we define a scenario named diffusion anomaly where the generated results of a reverse process under attack deviate significantly from the normal ones. By analyzing the underlying formation mechanism of the diffusion anomaly, we reveal how perturbations are amplified during the reverse process and accumulated in the results. Based on the analysis, we reveal the phenomena of divergence and homogeneity, which cause the diffusion process to deviate significantly from the normal process and to decline in diversity. Leveraging these two phenomena, we propose a method named Diffusion Anomaly Detection (DADet) to effectively detect both backdoor and adversarial attacks. Extensive experiments demonstrate that our proposal achieves excellent defense performance against backdoor and adversarial attacks. Specifically, for the backdoor attack detection, our method achieves an F1 score of 99% on different datasets, including MS COCO and CIFAR-10. For the detection of adversarial samples, the F1 score exceeds 84% across three adversarial attacks and two different tasks, evaluated on the MS COCO and Places365 datasets, respectively.