Backdoor Sentinel: Detecting and Detoxifying Backdoors in Diffusion Models via Temporal Noise Consistency
Read the original on arXiv AI →Backdoor Sentinel introduces Temporal Noise Consistency (TNC), a new phenomenon where backdoor activation disrupts noise prediction consistency across adjacent diffusion timesteps, while clean inputs remain stable. Leveraging TNC, the authors propose TNC-Defense, a closed‑loop gray‑box framework that includes TNC‑Detect for auditors to identify and localize anomalous timesteps without accessing model weights, and TNC‑Detox for service providers to perform trigger‑agnostic, timestep‑aware corrections that suppress backdoor behavior. Experiments on five backdoor attacks show an 11% improvement in detection accuracy and a 98.5% invalidation rate of triggered samples with minimal impact on generation quality.
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