  Deep learning models are well known to be susceptible to backdoor attack,
where the attacker only needs to provide a tampered dataset on which the
triggers are injected. Models trained on the dataset will passively implant the
backdoor, and triggers on the input can mislead the models during testing. Our
study shows that the model shows different learning behaviors in clean and
poisoned subsets during training. Based on this observation, we propose a
general training pipeline to defend against backdoor attacks actively. Benign
models can be trained from the unreliable dataset by decoupling the learning
process into three stages, i.e., supervised learning, active unlearning, and
active semi-supervised fine-tuning. The effectiveness of our approach has been
shown in numerous experiments across various backdoor attacks and datasets.
