  Deep Neural Networks (DNNs) have been widely used in many areas such as
autonomous driving and face recognition. However, DNN model is fragile to
backdoor attack. A backdoor in the DNN model can be activated by a poisoned
input with trigger and leads to wrong prediction, which causes serious security
issues in applications. It is challenging for current defenses to eliminate the
backdoor effectively with limited computing resources, especially when the
sizes and numbers of the triggers are variable as in the physical world. We
propose an efficient backdoor defense based on evolutionary trigger detection
and lightweight model repair. In the first phase of our method, CAM-focus
Evolutionary Trigger Filter (CETF) is proposed for trigger detection. CETF is
an effective sample-preprocessing based method with the evolutionary algorithm,
and our experimental results show that CETF not only distinguishes the images
with triggers accurately from the clean images, but also can be widely used in
practice for its simplicity and stability in different backdoor attack
situations. In the second phase of our method, we leverage several lightweight
unlearning methods with the trigger detected by CETF for model repair, which
also constructively demonstrate the underlying correlation of the backdoor with
Batch Normalization layers. Source code will be published after accepted.
