Backdoor Attacks on Contrastive Learning via Bi-level Trigger Optimization

Backdoor attacks can mislead contrastive learning feature extractors to associate trigger patterns with target classes, leading to misclassification of triggered inputs. The authors propose a bi-level optimization approach to identify a resilient backdoor trigger design that can maintain high similarity between triggered and target-class data in the embedding space, even under special contrastive learning mechanisms like data augmentation and uniformity.