CamoGAN: Evolving optimum camouflage with Generative Adversarial Networks

Abstract

Abstract One of the most challenging issues in modelling the evolution of protective colouration is the immense number of potential combinations of colours and textures. We describe CamoGAN, a novel method to exploit Generative Adversarial Networks to simulate an evolutionary arms race between the camouflage of a synthetic prey and its predator. Patterns evolved using our methods are shown to provide progressively more effective concealment and outperform two recognized camouflage techniques, as validated by using humans as visual predators. We believe CamoGAN will be highly useful, particularly for biologists, for rapidly developing and testing optimal camouflage or signalling patterns in multiple environments.