Face Forgery Detection via Multi-Feature Fusion and Local Enhancement

• Published in: IEEE transactions on circuits and systems for video technology 2024-09, Vol.34 (9), p.8972-8977
• Main Authors: Zhang, Dengyong, Chen, Jiahao, Liao, Xin, Li, Feng, Chen, Jiaxin, Yang, Gaobo
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Color; Deception; Deepfake; Deepfakes; face forgery detection; Faces; Feature extraction; Filters; Forgery; Frequency-domain analysis; Image enhancement; Modules; Noise; noise domain; spatial domain; Visual observation
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2024.3390945

With the rapid growth of Internet technology, security concerns have risen, particularly with the prevalence of Deepfakes, a popular visual forgery technique. Therefore, there is necessary to research more powerful methods to detect Deepfakes. However, many Convolutional Neural Networks-based detection methods struggle with cross-database performance, often overfitting to specific color textures. We observe that image noises can weaken the influence of color textures and expose the forgery traces in the noise domain. This is because tampering techniques, when altering face images, disrupt the consistency of feature distribution in the noise space. And the forgery traces in the noise space are complementary to the tampering artifacts present in the image space information. Therefore, we propose a novel face forgery detection network that combines spatial domain and noise domain. Our Dual Feature Fusion Module and Local Enhancement Attention Module contribute to more comprehensive feature representations, enhancing our method's discriminative ability. Experimental results demonstrate superior performance compared to existing methods on mainstream datasets. https://github.com/jhchen1998/DeepfakeDetection .