Family Reunion: Adversarial Machine Learning meets Digital Watermarking

Abstract

Artificial intelligence is increasingly employed in security-critical systems, such as autonomous cars and drones. Unfortunately, many machine learning techniques suffer from vulnerabilities that enable an adversary to thwart their successful application, either during the training or prediction phase. In this talk, we investigate this threat and discuss attacks against machine learning, such as ad- versarial perturbations and data poisoning. Surprisingly, several of the attacks are not entirely novel, and similar concepts have been developed independently for attacking digital watermarks in multimedia security. We review these similarities and provide links between the two research areas that may open new directions for improving both, machine learning and multimedia security.