  Indiscriminate data poisoning attacks are quite effective against supervised
learning. However, not much is known about their impact on unsupervised
contrastive learning (CL). This paper is the first to consider indiscriminate
poisoning attacks of contrastive learning. We propose Contrastive Poisoning
(CP), the first effective such attack on CL. We empirically show that
Contrastive Poisoning, not only drastically reduces the performance of CL
algorithms, but also attacks supervised learning models, making it the most
generalizable indiscriminate poisoning attack. We also show that CL algorithms
with a momentum encoder are more robust to indiscriminate poisoning, and
propose a new countermeasure based on matrix completion. Code is available at:
https://github.com/kaiwenzha/contrastive-poisoning.
