Countering Spoof: Towards Detecting Deepfake with Multidimensional Biological Signals. (22nd April 2021)

• Record Type: Journal Article
• Authors: Jin, Xinlei; Ye, Dengpan; Chen, Chuanxi
• Other Names: Chen Beijing Academic Editor.
• Is Part Of: Security and communication networks. Volume 2021(2021)
• Publication Date: 2021-04-22
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2021/6626974
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : The deepfake technology is conveniently abused with the low technology threshold, which may bring the huge social security risks. As GAN-based synthesis technology is becoming stronger, various methods are difficult to classify the fake content effectively. However, although the fake content generated by GANs can deceive the human eyes, it ignores the biological signals hidden in the face video. In this paper, we proposed a novel video forensics method with multidimensional biological signals, which extracting the difference of the biological signal between real and fake videos from three dimensions. The experimental results show that our method achieves 98% accuracy on the main public dataset. Compared with other technologies, the proposed method only extracts fake video information and is not limited to a specific generation method, so it is not affected by synthetic methods and has good adaptability.