  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.
