Transformer Based on Multi-Domain Feature Fusion for AI-Generated Image Detection
Abstract
With the rapid advancement of Generative Adversarial Networks (GANs), diffusion models, and other deep generative techniques, AI-generated images have achieved unprecedented levels of visual realism, posing severe challenges to the authenticity, security, and credibility of digital content. This paper proposes a novel hybrid transformer model that integrates spatial and frequency domains. It leverages CLIP to extract semantic inconsistencies in the image’s spatial domain while employing wavelet transforms to capture multi-scale frequency anomalies in AI-generated images. After cross-domain feature fusion, global modeling is performed within the Swin-Transformer architecture, enabling robust authenticity detection of AI-generated images. Extensive experiments demonstrate that our detector maintains high accuracy across diverse datasets.
Fields of Study & Subjects
Generative Adversarial Networks and Image Synthesis
Digital Media Forensic Detection
Adversarial Robustness in Machine Learning
Publication details
- Journal / source
- Electronics
- Publisher
- MDPI AG
- Volume
- 15
- Issue
- 3
- Pages
- 716–716
- Publication date
- 6 February 2026
- Language
- EN