  Self-supervised learning (SSL) is a prevalent approach for encoding data
representations. Using a pre-trained SSL image encoder and subsequently
training a downstream classifier, impressive performance can be achieved on
various tasks with very little labeled data. The growing adoption of SSL has
led to an increase in security research on SSL encoders and associated Trojan
attacks. Trojan attacks embedded in SSL encoders can operate covertly,
spreading across multiple users and devices. The presence of backdoor behavior
in Trojaned encoders can inadvertently be inherited by downstream classifiers,
making it even more difficult to detect and mitigate the threat. Although
current Trojan detection methods in supervised learning can potentially
safeguard SSL downstream classifiers, identifying and addressing triggers in
the SSL encoder before its widespread dissemination is a challenging task. This
challenge arises because downstream tasks might be unknown, dataset labels may
be unavailable, and the original unlabeled training dataset might be
inaccessible during Trojan detection in SSL encoders. We introduce SSL-Cleanse
as a solution to identify and mitigate backdoor threats in SSL encoders. We
evaluated SSL-Cleanse on various datasets using 1200 encoders, achieving an
average detection success rate of 82.2% on ImageNet-100. After mitigating
backdoors, on average, backdoored encoders achieve 0.3% attack success rate
without great accuracy loss, proving the effectiveness of SSL-Cleanse. The
source code of SSL-Cleanse is available at
https://github.com/UCF-ML-Research/SSL-Cleanse.
