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CamoGAN: Evolving optimum camouflage with Generative Adversarial Networks

Research output: Contribution to journalArticle

Original languageEnglish
Number of pages8
JournalMethods in Ecology and Evolution
Early online date7 Nov 2019
DOIs
DateSubmitted - 1 Oct 2018
DateAccepted/In press - 29 Oct 2019
DateE-pub ahead of print (current) - 7 Nov 2019

Abstract

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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  • Full-text PDF (final published version)

    Rights statement: This is the final published version of the article (version of record). It first appeared online via Wiley at https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13334. Please refer to any applicable terms of use of the publisher.

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    Licence: CC BY

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