[
    {
        "id": "osp-24839",
        "type": "article-journal",
        "title": "StegGNN: Learning Graphical Representation for Image Steganography",
        "author": [
            {
                "family": "Kumar",
                "given": "Abhinav"
            },
            {
                "family": "Singhal",
                "given": "Shorya"
            },
            {
                "family": "Pandey",
                "given": "Agam"
            },
            {
                "family": "Kumar",
                "given": "Tushar"
            },
            {
                "family": "Jindal",
                "given": "Sukrit"
            }
        ],
        "URL": "https://omanscience.com/en/articles/steggnn-learning-graphical-representation-for-image-steganography",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Image steganography refers to embedding secret messages within cover images while maintaining imperceptibility. Recent advances in deep learning - primarily driven by Convolutional Neural Networks (CNNs) and architectures such as inverse neural networks, autoencoders, and generative adversarial networks - have led to notable progress. However, these frameworks are primarily built on CNN architectures, which treat images as regular grids and are limited by their receptive field size and a bias toward spatial locality. In parallel, Graph Neural Networks (GNNs) have recently demonstrated strong adaptability in several computer vision tasks, achieving state-of-the-art performance with architectures such as Vision GNN (ViG). This work moves in that direction and introduces StegGNN - a novel autoencoder-based, cover-agnostic image steganography framework based on GNNs. By modeling images as graph structures, our approach leverages the representational flexibility of GNNs over the grid-based rigidity of conventional CNNs. We conduct extensive experiments on standard benchmark datasets to evaluate visual quality and imperceptibility. Our results show that our GNN-based method performs comparably to existing CNN benchmarks. These findings suggest that GNNs provide a promising alternative representation for steganographic embedding and open the field of deep learning-based steganography to further exploration of GNN-based architectures."
    }
]