  This paper explores the connection between steganography and adversarial
images. On the one hand, ste-ganalysis helps in detecting adversarial
perturbations. On the other hand, steganography helps in forging adversarial
perturbations that are not only invisible to the human eye but also
statistically undetectable. This work explains how to use these information
hiding tools for attacking or defending computer vision image classification.
We play this cat and mouse game with state-of-art classifiers, steganalyzers,
and steganographic embedding schemes. It turns out that steganography helps
more the attacker than the defender.
