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CamoGAN: Evolving optimum camouflage with Generative Adversarial Networks

Research output: Contribution to journalArticle

Original languageEnglish
JournalMethods in Ecology and Evolution
DateSubmitted - 1 Oct 2018
DateAccepted/In press (current) - 29 Oct 2019


1. One of the most challenging issues in modelling the evolution of protective colouration is the immense number of potential combinations of colours and textures.
2. 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.
3. Patterns evolved using our methods are shown to provide progressively more effective concealment and outperform two recognised camouflage techniques, as validated by using humans as visual predators.
4. We believe CamoGAN will be highly useful, particularly for biologists, for rapidly developing and testing optimal camouflage or signalling patterns in multiple environments.

    Structured keywords

  • Cognitive Science
  • Visual Perception

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