Backdoor attacks pose a serious security threat for training neural networks
as they surreptitiously introduce hidden functionalities into a model. Such
backdoors remain silent during inference on clean inputs, evading detection due
to inconspicuous behavior. However, once a specific trigger pattern appears in
the input data, the backdoor activates, causing the model to execute its
concealed function. Detecting such poisoned samples within vast datasets is
virtually impossible through manual inspection. To address this challenge, we
propose a novel approach that enables model training on potentially poisoned
datasets by utilizing the power of recent diffusion models. Specifically, we
create synthetic variations of all training samples, leveraging the inherent
resilience of diffusion models to potential trigger patterns in the data. By
combining this generative approach with knowledge distillation, we produce
student models that maintain their general performance on the task while
exhibiting robust resistance to backdoor triggers.

隐形功能型后门攻击对训练神经网络构成了严重的安全威胁，本文提出了一种基于扩散模型及知识蒸馏的新方法，能够在潜在受污染的数据集上训练模型，并生成具备对抗后门触发的鲁棒性的学生模型。