Abstract
In recent years, despite its wide use in various fields, deepfake has been abused to generate hazardous contents such as fake movies, rumors, and fake news by manipulating or replacing facial information of the original sources and, thus, exerts huge security threats to the society. Facing the continuous evolution of deepfake, research on active detection and prevention technology becomes particularly important. In this paper, we propose a new deepfake detection method based on cross-domain fusion, which, on the basis of traditional spatial domain features, realizes the fusion of cross-domain image features by introducing edge geometric features of the frequency domain and, therefore, achieves considerable improvements on classification accuracy. Further evaluations of this method have been performed on publicly deepfake datasets, and the results show that our method is effective particularly on the Meso-4 DeepFake Database.
Highlights
With the rapid development of the Internet of ings (IoT), society has entered a new era [1]. e IoT has many distinct advantages, such as security, real time, automation, embeddedness, interoperability, and interconnection
The deepfake technique, which has caused bad influence to the society recently, can generate realistic fake images, videos, and audio contents, and forge evidence for electronic crime. e abuse of this technique has seriously threatened the network information and data security of both individuals and the society. erefore, the research of deepfake detection technology is of great significance to ensure the authenticity of video, image, and audio transmitted in the network
E main contributions of this paper are as follows: (1) We propose a new deepfake detection approach to improve the detection accuracy, which fuses spatial domain features extracted from the image and features extracted from the frequency domain to capture more detailed forgery trails and get more comprehensive face features
Summary
With the rapid development of the Internet of ings (IoT), society has entered a new era [1]. e IoT has many distinct advantages, such as security, real time, automation, embeddedness, interoperability, and interconnection. With the rapid development of the Internet of ings (IoT), society has entered a new era [1]. In the context of the Internet of ings and 5G, the speed of the network is constantly improving, which brings convenience to people, and facilitates information fraud [2, 3]. Deep learning is booming as a new technology. The deepfake technique, which has caused bad influence to the society recently, can generate realistic fake images, videos, and audio contents, and forge evidence for electronic crime. E abuse of this technique has seriously threatened the network information and data security of both individuals and the society. Erefore, the research of deepfake detection technology is of great significance to ensure the authenticity of video, image, and audio transmitted in the network. Our method uses a network to gain the image’s spatial domain feature vectors and extracts the edge geometric features in frequency domain, in order to obtain a vector set containing both high-level semantic features and low-level edge features of the image
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