  Neural networks are widely known to be vulnerable to backdoor attacks, a
method that poisons a portion of the training data to make the target model
perform well on normal data sets, while outputting attacker-specified or random
categories on the poisoned samples. Backdoor attacks are full of threats.
Poisoned samples are becoming more and more similar to corresponding normal
samples, and even the human eye cannot easily distinguish them. On the other
hand, the accuracy of models carrying backdoors on normal samples is no
different from that of clean models.In this article, by observing the
characteristics of backdoor attacks, We provide a new model training method
(PT) that freezes part of the model to train a model that can isolate
suspicious samples. Then, on this basis, a clean model is fine-tuned to resist
backdoor attacks.
