  This paper applies theories about the Human Visual System to make Adversarial
AI more effective. To date, Adversarial AI has modeled perceptual distances
between clean and adversarial examples of images using Lp norms. These norms
have the benefit of simple mathematical description and reasonable
effectiveness in approximating perceptual distance. However, in prior decades,
other areas of image processing have moved beyond simpler models like Mean
Squared Error (MSE) towards more complex models that better approximate the
Human Visual System (HVS). We demonstrate a proof of concept of incorporating
HVS models into Adversarial AI.
