TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors
<|reference_start|>TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors: 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.<|reference_end|>
@article{mo2024terd:,
    title={TERD: A Unified Framework for Safeguarding Diffusion Models Against
  Backdoors},
    author={Yichuan Mo, Hui Huang, Mingjie Li, Ang Li, Yisen Wang},
    journal={International Conference on Machine Learning 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.05294},
    primaryClass={cs.CR cs.AI cs.LG}
}