Computation and Data Efficient Backdoor Attacks

Backdoor attacks against deep neural network (DNN) models have been widely studied. Various attack techniques have been proposed for different domains and paradigms, e.g., image, point cloud, natural language processing, transfer learning, etc. The most widely-used way to embed a backdoor into a DNN model is to poison the training data. They usually randomly select samples from the benign training set for poisoning, without considering the distinct contribution of each sample to the backdoor effectiveness, making the attack less optimal. A recent work [40] proposed to use the forgetting score to measure the importance of each poisoned sample and then filter out redundant data for effective backdoor training. However, this method is empirically designed without theoretical proofing. It is also very time-consuming as it needs to go through several training stages for data selection. To address such limitations, we propose a novel confidence-based scoring methodology, which can efficiently measure the contribution of each poisoning sample based on the distance posteriors. We further introduce a greedy search algorithm to find the most informative samples for backdoor injection more promptly. Experimental evaluations on both 2D image and 3D point cloud classification tasks show that our approach can achieve comparable performance or even surpass the forgetting score-based searching method while requiring only several extra epochs' computation of a standard training process. Our code can be found at https://github.com/WU-YU-TONG/ computational_efficient_backdoor