Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection

• URL: http://arxiv.org/abs/2212.05667v1
• Date: Mon, 12 Dec 2022 02:54:08 GMT
• Title: Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection
• Authors: Junke Wang, Zhenxin Li, Chao Zhang, Jingjing Chen, Zuxuan Wu, Larry S. Davis, Yu-Gang Jiang
• Abstract summary: Recent advances in deep learning, particularly deep generative models, open the doors for producing perceptually convincing images and videos at a low cost. This paper provides a comprehensive review of the current media tampering detection approaches, and discusses the challenges and trends in this field for future research.
• Score: 115.83992775004043
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online media data, in the forms of images and videos, are becoming mainstream communication channels. However, recent advances in deep learning, particularly deep generative models, open the doors for producing perceptually convincing images and videos at a low cost, which not only poses a serious threat to the trustworthiness of digital information but also has severe societal implications. This motivates a growing interest of research in media tampering detection, i.e., using deep learning techniques to examine whether media data have been maliciously manipulated. Depending on the content of the targeted images, media forgery could be divided into image tampering and Deepfake techniques. The former typically moves or erases the visual elements in ordinary images, while the latter manipulates the expressions and even the identity of human faces. Accordingly, the means of defense include image tampering detection and Deepfake detection, which share a wide variety of properties. In this paper, we provide a comprehensive review of the current media tampering detection approaches, and discuss the challenges and trends in this field for future research.

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