Camouflaged　object　detection　(COD)　aims　to　resolve　the　tough　issue　of　accurately　segmenting　objects　hidden　in　the　surroundings.　However,　the　existing　methods　suffer　from　two　major　problems:　the　incomplete　interior　and　the　inaccurate　boundary　of　the　object.　To　address　these　difficulties,　we　propose　a　three-stage　skeletonboundary-guided　network　(SBGNet)　for　the　COD　task.　Specifically,　we　design　a　novel　skeleton-boundary　label　to　be　complementary　to　the　typical　pixel-wise　mask　annotation,　emphasizing　the　interior　skeleton　and　the　boundary　of　the　camouflaged　object.　Furthermore,　the　proposed　feature　guidance　module　(FGM)　leverages　the　skeleton-boundary　feature　to　guide　the　model　to　focus　on　both　the　interior　and　the　boundary　of　the　camouflaged　object.　Besides,　we　design　a　bidirectional　feature　flow　path　with　the　information　interaction　module　(IIM)　to　propagate　and　integrate　the　semantic　and　texture　information.　Finally,　we　propose　the　dual　feature　distillation　module　(DFDM)　to　progressively　refine　the　segmentation　results　in　a　fine-grained　manner.　Comprehensive　experiments　demonstrate　that　our　SBGNet　outperforms　20　state-of-the-art　methods　on　three　benchmarks　in　both　qualitative　and　quantitative　comparisons.　CCS　Concepts:　center　dot　Computing　methodologies　-＞　Scene　understanding;