_NeurIPS2023_CR__Certified_Backdoor_Detection.pdf

The main purpose of this research is to provide the user of DNN classifiers with a method to detect if the model is backdoor attacked without access to the training set. All attacks used to evaluate our detection method in this paper are created by published backdoor attack strategies on public datasets. Thus, we did not create new threats to society. Moreover, our work provides a new perspective on backdoor defense, as it is the first to address the certification of backdoor detection. It helps other researchers to understand the behavior of deep learning systems facing malicious activities. While existing backdoor detectors are all empirical [67, 20, 75, 41, 69, 6, 56, 13], our work initiates a new research direction - backdoor detection with certification. Moreover, we first exposed that certified backdoor detectors and certified robustness against backdoor attacks complement each other [86, 71, 27, 53].