TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors

• URL: http://arxiv.org/abs/2409.05294v1
• Date: Mon, 9 Sep 2024 03:02:16 GMT
• Title: TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors
• Authors: Yichuan Mo, Hui Huang, Mingjie Li, Ang Li, Yisen Wang,
• Abstract summary: Diffusion models are vulnerable to backdoor attacks that compromise their integrity. We propose TERD, a backdoor defense framework that builds unified modeling for current attacks. TERD secures a 100% True Positive Rate (TPR) and True Negative Rate (TNR) across datasets of varying resolutions.
• Score: 36.07978634674072
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have achieved notable success in image generation, but they remain highly vulnerable to backdoor attacks, which compromise their integrity by producing specific undesirable outputs when presented with a pre-defined trigger. In this paper, we investigate how to protect diffusion models from this dangerous threat. Specifically, we propose TERD, a backdoor defense framework that builds unified modeling for current attacks, which enables us to derive an accessible reversed loss. A trigger reversion strategy is further employed: an initial approximation of the trigger through noise sampled from a prior distribution, followed by refinement through differential multi-step samplers. Additionally, with the reversed trigger, we propose backdoor detection from the noise space, introducing the first backdoor input detection approach for diffusion models and a novel model detection algorithm that calculates the KL divergence between reversed and benign distributions. Extensive evaluations demonstrate that TERD secures a 100% True Positive Rate (TPR) and True Negative Rate (TNR) across datasets of varying resolutions. TERD also demonstrates nice adaptability to other Stochastic Differential Equation (SDE)-based models. Our code is available at https://github.com/PKU-ML/TERD.

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