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ORIGINAL RESEARCH article

Front. Artif. Intell.

Sec. AI in Finance

Volume 8 - 2025 | doi: 10.3389/frai.2025.1663292

This article is part of the Research TopicNew Trends in AI-Generated Media and SecurityView all 5 articles

Multi-Modal Texture Fusion Network for Detecting AI-Generated Images

Provisionally accepted
  • School of Public Policy and Administration, Nanchang University, Nanchang, China

The final, formatted version of the article will be published soon.

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.

Keywords: AI-generated content, image processing, Multimedia forensics, Texture Analysis, Multi-Modal

Received: 15 Jul 2025; Accepted: 30 Sep 2025.

Copyright: © 2025 Yu and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Haozheng Yu, yuhaozheng18@email.ncu.edu.cn

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