CamoGAN: Evolving optimum camouflage with Generative Adversarial Networks

Laszlo Talas*, John Fennell, Karin Kjernsmo, Innes Cuthill, Nick Scott-Samuel, Roland Baddeley

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

3 Citations (Scopus)
684 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)240-247
Number of pages8
JournalMethods in Ecology and Evolution
Issue number2
Early online date7 Nov 2019
Publication statusPublished - 1 Feb 2020

Structured keywords

  • Cognitive Science
  • Visual Perception


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