Existing　weakly-supervised　camouflaged　object　detection　(WSCOD)　methods　have　much　difficulty　in　detecting　accurate　object　boundaries　due　to　insufficient　and　imprecise　boundary　supervision　in　scribble　annotations.　Drawing　inspiration　from　human　perception　that　discerns　camouflaged　objects　by　incorporating　both　object　region　and　boundary　information,　we　propose　a　novel　Mutual　Interaction　Network　(MiNet)　for　scribble-based　WSCOD　to　alleviate　the　detection　difficulty　caused　by　insufficient　scribbles.　The　proposed　MiNet　facilitates　mutual　reinforcement　between　region　and　edge　cues,　thereby　integrating　more　robust　priors　to　enhance　detection　accuracy.　In　this　paper,　we　first　construct　an　edge　cue　refinement　net,　featuring　a　core　region-aware　guidance　module　(RGM)　aimed　at　leveraging　the　extracted　region　feature　as　a　prior　to　generate　the　discriminative　edge　map.　By　considering　both　object　semantic　and　positional　relationships　between　edge　feature　and　region　feature,　RGM　highlights　the　areas　associated　with　the　object　in　the　edge　feature.　Subsequently,　to　tackle　the　inherent　similarity　between　camouflaged　objects　and　the　surroundings,　we　devise　a　region-boundary　refinement　net.　This　net　incorporates　a　core　edge-aware　guidance　module　(EGM),　which　uses　the　enhanced　edge　map　from　the　edge　cue　refinement　net　as　guidance　to　refine　the　object　boundaries　in　an　iterative　and　multi-level　manner.　Experiments　on　CAMO,　CHAMELEON,　COD10K,　and　NC4K　datasets　demonstrate　that　the　proposed　MiNet　outperforms　the　state-of-the-art　methods.　©　2024　ACM.