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 recognized 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.

中文翻译：

---

CamoGAN：通过生成对抗网络进化出最佳伪装

1. 在对保护性着色的演变进行建模时，最具挑战性的问题之一是颜色和纹理的大量潜在组合。
2. 我们描述了CamoGAN，这是一种利用生成对抗网络模拟合成猎物伪装与其捕食者之间的进化军备竞赛的新颖方法。
3. 通过使用我们的方法演变而来的图案显示出可以提供越来越有效的遮盖力，并且胜过了两种公认的伪装技术，这种伪装技术已被人类用作视觉掠食者进行了验证。
4. 我们相信CamoGAN对于在多种环境中快速开发和测试最佳伪装或信号模式特别有用，特别是对生物学家而言。