Simi D Kuniyilh, Rita Machacy
This paper reviews the vulnerabilities of contrastive learning to backdoor attacks and discusses potential defenses and future research directions.
Contrastive learning is a popular method used in artificial intelligence to help machines understand and represent data. However, it has been found to be vulnerable to backdoor attacks, where attackers insert hidden malicious behaviors into the system. This paper reviews how these attacks work, highlights the areas where contrastive learning is particularly susceptible, and discusses ways to defend against such attacks. The findings are important for ensuring the security of AI systems, especially in industries where data integrity is crucial.
