Abstract: As a challenging fundamental research in computer vision area, camouflaged object detection (COD) usually take scribble annotations as weakly supervised signals due to the high cost of pixel-level annotations. To overcome the inherent limitations of scribble annotations with sparse information and lack of edge information, a novel Interactive Graphical Reasoning Network (IGRNet) was proposed in this paper, inferring the intrinsic relationships between the camouflaged regions and their edges with graph representations to improve the prediction reliability of models. Specifically, a graph inference network was introduced to model long-range dependencies between pixels, and an efficient Graph Interaction Unit (GIU) was designed to enhance the representation of heterogeneous features. Meanwhile, in order to improve the scene understanding ability of the model and make full use of the complementarity between different features, a Context Enhancement Module (CEM) was constructed to achieve multi-feature fusion and contextual information mining. In addition, a self-supervised camouflage detection loss (L_scd ) was proposed to guide the network to learn structural information and further improve the foreground-background distinction ability. Extensive experiment results on three standard benchmark datasets show that the proposed method can not only significantly outperform existing weakly-supervised algorithms, but even surpass the performance of fully-supervised methods in some evaluation methods.