PAPnet: A Plug-and-play Virus Network for Backdoor Attack

Most existing backdoor attacks focus on designing various trigger injection methods and fine-tuning victim networks, which are difficult to deploy in real-world applications. In this paper, we propose a plug-and-play virus network, dubbed PAPnet, for backdoor attack. PAPnet is a lightweight network with the same dimensional output as the victim network. In the training stage, we only need the output of the victim network and train PAPnet to learn from poisoned data and clean data. This makes PAPnet easier to learn than fine-tunebased backdoor attack methods. Besides, PAPnet can be easily attached to the different classification network models without modifying the architecture of the victim network and fine-tuning processing. We have conducted various experiments on four datasets with four classical classification networks. Experimental results demonstrate the superiority of our proposed method.