Not all camouflages are equally effective, as even a partially visible contour or a slight color difference can make the animal stand out and break its camouflage. In this paper, we address the question of what makes a camouflage successful, by proposing three scores for automatically assessing its effectiveness. In particular, we show that camouflage can be measured by the similarity between background and foreground features and boundary visibility. We use these camouflage scores to assess and compare all available camouflage datasets. We also incorporate the proposed camouflage score into a generative model as an auxiliary loss and show that effective camouflage images or videos can be synthesised in a scalable manner. The generated synthetic dataset is used to train a transformer-based model for segmenting camouflaged animals in videos. Experimentally, we demonstrate state-of-the-art camouflage breaking performance on the public MoCA-Mask benchmark.

翻译：并非所有伪装都同样有效，即便是部分可见的轮廓或细微的色差，也可能使动物突显并破坏其伪装。本文通过提出三种自动评估伪装有效性的评分指标，探讨了伪装成功的关键因素。具体而言，我们证明伪装程度可通过背景与前景特征的相似性以及边界可见性进行量化。利用这些伪装评分，我们对所有公开的伪装数据集进行了评估与比较。此外，我们将所提出的伪装评分作为辅助损失函数融入生成模型，证明了能以可扩展的方式合成有效的伪装图像或视频。利用生成的合成数据集，我们训练了一个基于Transformer的模型，用于分割视频中的伪装动物。实验结果表明，该方法在公开的MoCA-Mask基准上达到了最先进的伪装破解性能。