New technique protects contrastive ML against adversarial attacks

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Contrastive learning (CL) is a machine learning technique that has gained popularity in the past few years because it reduces the need for annotated data, one of the main pain points of developing ML models. But due to its peculiarities, contrastive learning presents security challenges that are different from those found in supervised machine learning. Machine learning and security researchers are worried about the effect of adversarial attacks on ML models trained through contrastive learning. Accepted at NeurIPS 2021, the paper introduces a new technique that helps protect contrastive learning models against adversarial attacks while also preserving their accuracy. Supervised learning, the traditional way of training ML models, requires large sets of labeled data.