Multi-modal texture fusion network for detecting AI-generated images

Abstract

With the rapid advancement of AI-generated content, detecting synthetic images has become a critical task in digital forensics and media integrity. In this paper, we propose a novel multi-modal fusion network that leverages complementary texture and content information to improve the detection of AI-generated images. Our approach integrates three input branches: the original RGB image, a local binary pattern (LBP) map to capture micro-texture irregularities, and a gray-level co-occurrence matrix (GLCM) representation to encode statistical texture dependencies. These three streams are processed in parallel through a shared-weight convolutional backbone and subsequently fused at the feature level to enhance discrimination capability. Extensive experiments conducted on benchmark datasets demonstrate that our method outperforms existing single-modality baselines and achieves strong generalization across multiple types of generative models. The proposed fusion framework offers an interpretable and efficient solution for robust and reliable detection of AI-synthesized imagery.