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Online media data, in the forms of images and videos, are becoming mainstreamcommunication channels. However, recent advances in deep learning, particularlydeep generative models, open the doors for producing perceptually convincingimages and videos at a low cost, which not only poses a serious threat to thetrustworthiness of digital information but also has severe societalimplications. This motivates a growing interest of research in media tamperingdetection, i.e., using deep learning techniques to examine whether media datahave been maliciously manipulated. Depending on the content of the targetedimages, media forgery could be divided into image tampering and Deepfaketechniques. The former typically moves or erases the visual elements inordinary images, while the latter manipulates the expressions and even theidentity of human faces. Accordingly, the means of defense include imagetampering detection and Deepfake detection, which share a wide variety ofproperties. In this paper, we provide a comprehensive review of the currentmedia tampering detection approaches, and discuss the challenges and trends inthis field for future research.

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