  Relying only on unlabeled data, Self-supervised learning (SSL) can learn rich
features in an economical and scalable way. As the drive-horse for building
foundation models, SSL has received a lot of attention recently with wide
applications, which also raises security concerns where backdoor attack is a
major type of threat: if the released dataset is maliciously poisoned,
backdoored SSL models can behave badly when triggers are injected to test
samples. The goal of this work is to investigate this potential risk. We notice
that existing backdoors all require a considerable amount of \emph{labeled}
data that may not be available for SSL. To circumvent this limitation, we
explore a more restrictive setting called no-label backdoors, where we only
have access to the unlabeled data alone, where the key challenge is how to
select the proper poison set without using label information. We propose two
strategies for poison selection: clustering-based selection using pseudolabels,
and contrastive selection derived from the mutual information principle.
Experiments on CIFAR-10 and ImageNet-100 show that both no-label backdoors are
effective on many SSL methods and outperform random poisoning by a large
margin. Code will be available at https://github.com/PKU-ML/nlb.
