Seed, Diffuse, and Refine: Reliability-Screened Prior Learning for Unsupervised Camouflaged Object Detection

Unsupervised Camouflaged Object Detection (UCOD) aims to eliminate the dependence on costly pixel-level annotations, yet remains highly challenging due to the extremely low foreground-background contrast and the absence of reliable supervision. Recent UCOD approaches increasingly exploit priors from foundation models to alleviate annotation scarcity, but often directly propagate these priors without explicitly assessing their reliability, resulting in noisy pseudo supervision and limited robustness in complex camouflage scenarios. In this paper, we revisit UCOD from the perspective of prior reliability and propose a reliability-screened framework that selectively exploits foundation-model knowledge. Instead of directly inheriting model-generated priors, we leverage the rejection-aware capability of SAM3 to perform semantic probing and construct a SAM3-screened seed set for subsequent learning. Based on these screened but limited seeds, we introduce a diffusion-based segmentation head to capture the distributional characteristics of camouflage structures rather than directly fitting deterministic masks. Furthermore, we propose a SAM3-guided boundary refinement strategy, where diffusion predictions are converted into geometric prompts to adapt SAM3 for boundary-aware mask refinement. Finally, we develop a dual-sided distillation scheme that transfers complementary geometric and semantic priors to the final segmentation model. Extensive experiments on three COD benchmarks demonstrate that our method achieves state-of-the-art performance among UCOD methods.