  Self-supervised learning models are vulnerable to backdoor attacks. Existing
backdoor attacks that are effective in self-supervised learning often involve
noticeable triggers, like colored patches, which are vulnerable to human
inspection. In this paper, we propose an imperceptible and effective backdoor
attack against self-supervised models. We first find that existing
imperceptible triggers designed for supervised learning are not as effective in
compromising self-supervised models. We then identify this ineffectiveness is
attributed to the overlap in distributions between the backdoor and augmented
samples used in self-supervised learning. Building on this insight, we design
an attack using optimized triggers that are disentangled to the augmented
transformation in the self-supervised learning, while also remaining
imperceptible to human vision. Experiments on five datasets and seven SSL
algorithms demonstrate our attack is highly effective and stealthy. It also has
strong resistance to existing backdoor defenses. Our code can be found at
https://github.com/Zhang-Henry/IMPERATIVE.
