Architected Graph-Enhanced Neural Network Framework for Image Integrity and Tamper Precision

Abstract

Image authenticity is a perennial issue with the evolution of advanced tampering techniques, particularly grid-aligned manipulations and spatial vulnerability-exploiting post-processing attacks. The paper presents a novel architecture for a neural network fusing Graph Neural Networks (GNNs), Convolutional Neural Networks (CNNs), and digital watermarking to detect tampering successfully and localize it. CNNs are trained on learning local spatial features, and an invisible low-dropout convolutional encoder places watermarks to ensure authenticity. GNNs address the inherent problem of modeling long-range structural relations for blind tampering pattern detection that is accurate. With a graph-based representation of image blocks, the framework learns complex spatial relations, which alleviates the rigid receptive field limitation. Extensive experiments on benchmark datasets confirm the framework’s superiority, achieving an F1-Score of 0.94 in tampering localization, which significantly outperforms the 0.88 F1-Score of leading state-of-the-art methods. This approach creates a new standard for image integrity verification, offering an interpretable and scalable solution with far-reaching applications in digital content protection