Recently, backdoor attacks have posed a serious security threat to the training process of deep neural networks (DNNs). The attacked model behaves normally on benign samples but outputs a specific result when the trigger is present. However, compared with the rocketing progress of backdoor attacks, existing defenses are difficult to deal with these threats effectively or require benign samples to work, which may be unavailable in real scenarios. In this paper, we find that the poisoned samples and benign samples can be distinguished with prediction entropy. This inspires us to propose a novel dual-network training framework: The Victim and The Beneficiary (V&B), which exploits a poisoned model to train a clean model without extra benign samples. Firstly, we sacrifice the Victim network to be a powerful poisoned sample detector by training on suspicious samples. Secondly, we train the Beneficiary network on the credible samples selected by the Victim to inhibit backdoor injection. Thirdly, a semi-supervised suppression strategy is adopted for erasing potential backdoors and improving model performance. Furthermore, to better inhibit missed poisoned samples, we propose a strong data augmentation method, AttentionMix, which works well with our proposed V&B framework. Extensive experiments on two widely used datasets against 6 state-of-the-art attacks demonstrate that our framework is effective in preventing backdoor injection and robust to various attacks while maintaining the performance on benign samples. Our code is available at https://github.com/Zixuan-Zhu/VaB.

通过使用预测熵来区分污染样本和良性样本，本文提出了一种新的双网络训练框架：受害者和受益者 (V&B)，通过在可疑样本上训练受害者网络来检测有毒样本，然后用受害者选出的可信样本训练受益者网络以抑制后门注入，同时采用半监督抑制策略以消除潜在的后门，并提出了AttentionMix强大的数据增强方法来更好地抑制被错过的有毒样本，大量实验证明了我们的框架在防止后门注入和对抗各种攻击方面的有效性，并能保持良性样本上的性能。

受害者与受益者：利用被注入毒数据的模型训练干净数据的模型