PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks

It is widely known that state-of-the-art machine learning models, including vision and language models, can be seriously compromised by adversarial perturbations. It is therefore increasingly relevant to develop capabilities to certify their performance in the presence of the most effective adversarial attacks. Our paper offers a new approach to certify the performance of machine learning models in the presence of adversarial attacks with population level risk guarantees. In particular, we introduce the notion of $(\alpha,\zeta)$ machine learning model safety. We …

adversarial adversarial attacks art attacks capabilities certification cs.lg language language models machine machine learning machine learning models paper performance state vision