BMAIU: Backdoor Mitigation in Self-Supervised Learning Through Active Implantation and Unlearning

Self-supervised learning (SSL) is vulnerable to backdoor attacks, while the downstream classifiers based on SSL models inevitably inherit these backdoors, even when they are trained on clean samples. Despite the proposal of several methods of backdoor defense against backdoor attacks, few methods remain that can be used to effectively defend against various backdoor attacks while maintaining the high performance of the model. In this paper, based on the discovery that unlearning any trigger enhances the overall backdoor robustness of the model, a novel, efficient, and straightforward approach is proposed to address the most advanced backdoor attacks. This method involves two stages. Firstly, a backdoor is actively implanted in the model with a custom trigger. Secondly, the model is fine-tuned to unlearn the custom trigger. Through these two stages, it is found out that not only is the implanted backdoor removed, but the unknown backdoors implanted by attackers are also effectively mitigated. As illustrated by the extensive experiments conducted on multiple datasets against the current state-of-the-art methods of attack, the method proposed in this paper requires only a small amount of clean data (approximately 1%) to reduce the success rate of backdoor attacks effectively while ensuring minimal impact on model performance.