  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.
