GhostEncoder: Stealthy Backdoor Attacks with Dynamic Triggers to
  Pre-trained Encoders in Self-supervised Learning
Qiannan Wang Changchun Yin Zhe Liu Liming Fang Run Wang Chenhao Lin
Within the realm of computer vision, self-supervised learning (SSL) pertains
to training pre-trained image encoders utilizing a substantial quantity of
unlabeled images. Pre-trained image encoders can serve as feature extractors,
facilitating the construction of downstream classifiers for various tasks.
However, the use of SSL has led to an increase in security research related to
various backdoor attacks. Currently, the trigger patterns used in backdoor
attacks on SSL are mostly visible or static (sample-agnostic), making backdoors
less covert and significantly affecting the attack performance. In this work,
we propose GhostEncoder, the first dynamic invisible backdoor attack on SSL.
Unlike existing backdoor attacks on SSL, which use visible or static trigger
patterns, GhostEncoder utilizes image steganography techniques to encode hidden
information into benign images and generate backdoor samples. We then fine-tune
the pre-trained image encoder on a manipulation dataset to inject the backdoor,
enabling downstream classifiers built upon the backdoored encoder to inherit
the backdoor behavior for target downstream tasks. We evaluate GhostEncoder on
three downstream tasks and results demonstrate that GhostEncoder provides
practical stealthiness on images and deceives the victim model with a high
attack success rate without compromising its utility. Furthermore, GhostEncoder
withstands state-of-the-art defenses, including STRIP, STRIP-Cl, and
SSL-Cleanse.