Work

An adaptive dual-domain feature representation method for enhanced deep forgery detection

Year: 2025

Type: article

Abstract: Deep forgery detection technologies are crucial for image and video recognition tasks, with their performance heavily reliant on the features extracted from both real and fake images. However, most existing methods primarily focus on spatial domain features, which limits their accuracy. To address this limitation, we propose an adaptive dual-domain feature representation method for enhanced deep forgery detection. Specifically, an adaptive region dynamic convolution module is established to efficiently extract facial features from the spatial domain. Then, we introduce an adaptive frequency dynamic filter to capture effective frequency domain features. By fusing both spatial and frequency domain features, our approach significantly improves the accuracy of classifying real and fake facial images. Finally, experimental results on three real-world datasets validate the effectiveness of our dual-domain feature representation method, which substantially improves classification precision.

Source: Journal of Automation and Intelligence

DOI: 10.1016/j.jai.2025.11.003

Authors: Ming Li, Yan Qin, Heng Zhang, Zhiguo Shi

Institutions: Jiangsu Ocean University, Chongqing University, Zhejiang University

Language: English

Topic: Digital Media Forensic Detection

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Natural Science Foundation of China, Jiangsu Ocean University

Awards: G1410141852, G4688665460