The Camouflage Machine: Optimizing protective coloration using deep learning with genetic algorithms

John G Fennell*, László Tálas, Roland J Baddeley, Innes C Cuthill, Nicholas E Scott-Samuel

*Corresponding author for this work

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

2 Citations (Scopus)
82 Downloads (Pure)


The essential problem in visual detection is separating an object from its background. Whether in nature or human conflict, camouflage aims to make the problem harder, while conspicuous signals (e.g. for warning or mate attraction) require the opposite. Our goal is to provide a reliable method for identifying the hardest and easiest to find patterns, for any given environment. The problem is challenging because the parameter space provided by varying natural scenes and potential patterns is vast. Here we successfully solve the problem using deep learning with genetic algorithms and illustrate our solution by identifying appropriate patterns in two environments. To show the generality of our approach, we do so for both trichromatic and dichromatic visual systems. Patterns were validated using human participants; those identified as the best camouflage were significantly harder to find than a widely adopted military camouflage pattern, while those identified as most conspicuous were significantly easier than other patterns. Our method, dubbed the "Camouflage Machine", will be a useful tool for those interested in identifying the most effective patterns in a given context.
Original languageEnglish
Pages (from-to)614-624
Number of pages11
Issue number3
Early online date7 Jan 2021
Publication statusPublished - 18 Jan 2021

Structured keywords

  • Cognitive Science
  • Visual Perception


  • Camouflage
  • deep learning
  • genetic algorithms
  • optimization
  • protective coloration


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