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

TL;DR

This paper introduces a new AI system that detects fake images by combining texture and content analysis.

Contribution

The novel contribution is a multi-modal fusion network using RGB, LBP, and GLCM for improved AI-generated image detection.

Findings

The fusion network outperforms single-modality baselines in detecting AI-generated images.

The method generalizes well across multiple types of generative models.

The approach provides interpretable and efficient detection of synthetic imagery.

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.

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