In recent years, public security incidents caused by deepfake technology have occurred frequently around the world, which makes an efficient and accurate deepfake detection model crucial. The existing advanced methods use the manipulation features in the image to realize the binary classification of real and fake images by training complex neural network models. However, these models rely on a single manipulation feature, and the detection accuracy of these methods will be greatly reduced when the forgery technology or image quality of the training data set and the validation data set are different. Inspired by the existing work, we propose a two-stream collaborative learning framework that combines spatial texture differences and frequency information. The average difference convolution (ADC) is designed to extract the spatial texture difference information of the image, and the gray image frequency-aware decomposition (GFAD) is used to extract the artifact information of the image in the frequency domain. At the same time, the ViT idea is combined with cross attention mechanism for feature fusion to comprehensively mine forged features in forged images. Experimental results show that the proposed model has good detection effects on three benchmark data sets. In terms of cross-dataset evaluation, the AUC on Celeb-DF dataset reaches 82.86%, which is better than the existing advanced methods.

Shuaijv Fang

Zhiyong Zhang

Bin Song

ACM Transactions on Privacy and Security

ACM TRANSACTIONS ON PRIVACY AND SECURITY (TOPS)（前身为 TISSEC）发布信息和系统安全与隐私领域的高质量研究成果。欢迎研究涉及这些领域的各个方面，从技术到系统和应用，再到政策制定。

ACM Transactions on Privacy and Security (TOPS), formerly known as TISSEC, publishes high-quality research findings in the fields of information and system security and privacy. We welcome research covering all aspects of these areas, from technology to systems and applications, as well as policy-making.

《ACM Transactions on Privacy and Security》（前身为 TISSEC）致力于发布信息和系统安全及隐私领域的高质量研究成果。我们欢迎涉及这些领域各个方面的研究，包括技术、系统与应用，以及政策制定。

Category	Quartile
计算机科学	4
计算机科学, 计算机信息系统	4

Deepfake Detection Model Combining Texture Differences and Frequency Domain Information

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Journal Info

Category	Quartile
COMPUTER SCIENCE, INFORMATION SYSTEMS	2