Influence-Based Fair Selection for Sample-Discriminative Backdoor Attack

Backdoor attacks have posed a serious threat in machine learning models, wherein adversaries can poison training samples with maliciously crafted triggers to compromise the victim model. Advanced backdoor attack methods have focused on selectively poisoning more vulnerable training samples, achieving a higher attack success rate (ASR). However, we found that when the manipulation strength of the trigger is constrained to a very small value for imperceptible attacks, they suffer from extremely uneven class-wise ASR due to the unequal selection of instances per class. To solve this issue, we propose a novel backdoor attack method based on Influence-based Fair Selection (IFS), including two objectives: 1) selecting samples that significantly contribute to ASR and 2) ensuring class balance during the selection process. Specifically, we adapt Influence Functions, a classic technique in robust statistics, to evaluate the influence of trigger-embedded training samples on ASR. In this case, training samples contributing to reducing the backdoored test risk could possess higher influence scores. Further, a group-based pruning strategy is designed to avoid calculating the influence on ASR for all training samples, thereby significantly reducing the computational cost. Then, based on the influence score, we design an adaptive thresholding scheme to dynamically select samples with higher influence while maintaining class balance. Extensive experiments on four datasets verify the effectiveness of IFS compared with advanced methods.