Camouflaged object detection (COD) is the challenging task of identifying camouflaged objects visually blended into surroundings. Albeit achieving remarkable success, existing COD detectors still struggle to obtain precise results in some challenging cases. To handle this problem, we draw inspiration from the prey-vs-predator game that leads preys to develop better camouflage and predators to acquire more acute vision systems and develop algorithms from both the prey side and the predator side. On the prey side, we propose an adversarial training framework, Camouflageator, which introduces an auxiliary generator to generate more camouflaged objects that are harder for a COD method to detect. Camouflageator trains the generator and detector in an adversarial way such that the enhanced auxiliary generator helps produce a stronger detector. On the predator side, we introduce a novel COD method, called Internal Coherence and Edge Guidance (ICEG), which introduces a camouflaged feature coherence module to excavate the internal coherence of camouflaged objects, striving to obtain more complete segmentation results. Additionally, ICEG proposes a novel edge-guided separated calibration module to remove false predictions to avoid obtaining ambiguous boundaries. Extensive experiments show that ICEG outperforms existing COD detectors and Camouflageator is flexible to improve various COD detectors, including ICEG, which brings state-of-the-art COD performance.

翻译：伪装目标检测（COD）是识别视觉上融入背景的伪装目标的挑战性任务。尽管现有COD检测器已取得显著成功，但在某些困难场景下仍难以获得精确结果。为解决这一问题，我们从猎物与捕食者博弈中获得启发——该博弈促使猎物发展更优伪装能力，同时驱使捕食者进化出更敏锐的视觉系统——并分别从猎物侧与捕食者侧开发算法。在猎物侧，我们提出对抗训练框架Camouflageator，通过引入辅助生成器来生成更难以被COD方法检测的伪装物体。Camouflageator以对抗方式训练生成器与检测器，使得增强后的辅助生成器有助于产生更强的检测器。在捕食者侧，我们提出名为内部一致性与边缘引导（ICEG）的新型COD方法，该方法引入伪装特征一致性模块以挖掘伪装物体的内部一致性，力求获得更完整的分割结果。此外，ICEG提出新颖的边缘引导分离校准模块来消除错误预测，避免产生模糊边界。大量实验表明，ICEG优于现有COD检测器，且Camouflageator可灵活提升包括ICEG在内的多种COD检测器性能，从而实现了最先进的COD性能。