Apple of Sodom: Hidden Backdoors in Superior Sentence Embeddings via Contrastive Learning

Understand

This paper finds that contrastive learning can produce superior sentence embeddings for pre-trained models but is also vulnerable to backdoor attacks.

• We present the first backdoor attack framework, BadCSE, for state-of-the-art sentence embeddings under supervised and unsupervised learning settings.
• The attack manipulates the construction of positive and negative pairs so that the backdoored samples have a similar embedding with the target sample (targeted attack) or the negative embedding of its clean version (non-targeted attack).
• By injecting the backdoor in sentence embeddings, BadCSE is resistant against downstream fine-tuning.