Data-Free Backdoor Model Inspection: Masking and Reverse Engineering Loops for Feature Counting

Deep Neural Networks (DNNs) are widely used for the outstanding performance in many fields. However, the training of DNN models has high requirements for the users' data and computation resources, so many users with limited resources tend to download pre-trained models from some platforms and then finetune the pre-trained models to match their own tasks. However, the pre-trained models are under the threat of the backdoor attack. The backdoor attackers inject backdoors in the models, leading the backdoor models to predict target predictions designed by the attackers in advance. However, most existing backdoor model inspection methods rely on the clean data samples from the dataset of the model, which are difficult to get for users who just download the pre-trained models from the platforms. There are also a few defense methods not dependent on the data, but they also have their limits in practice. We propose Data-Free Masking and Reverse Engineering Loops (DF-MREL), a simple yet efficient data-free method for backdoor model inspection, which is widely applicable when resources are limited. Our experiments show its excellent performance in detecting backdoor models. Source code will be published after accepted.