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A review of digital video tampering: from simple editing to full synthesis.

Johnston, Pamela; Elyan, Eyad



Video tampering methods have witnessed considerable progress in recent years. This is partly due to the rapid development of advanced deep learning methods, and also due to the large volume of video footage that is now in the public domain. Historically, convincing video tampering has been too labour intensive to achieve on a large scale. However, recent developments in deep learning-based methods have made it possible not only to produce convincing forged video but also to fully synthesize video content. Such advancements provide new means to improve visual content itself, but at the same time, they raise new challenges for state-of-the-art tampering detection methods. Video tampering detection has been an active field of research for some time, with periodic reviews of the subject. However, little attention has been paid to video tampering techniques themselves. This paper provides an objective and in-depth examination of current techniques related to digital video manipulation. We thoroughly examine their development, and show how current evaluation techniques provide opportunities for the advancement of video tampering detection. A critical and extensive review of photo-realistic video synthesis is provided with emphasis on deep learning-based methods. Existing tampered video datasets are also qualitatively reviewed and critically discussed. Finally, conclusions are drawn upon an exhaustive and thorough review of tampering methods with discussions of future research directions aimed at improving detection methods.


JOHNSTON, P. and ELYAN, E. 2019. A review of digital video tampering: from simple editing to full synthesis. Digital investigation [online], 29, pages 67-81. Available from:

Journal Article Type Article
Acceptance Date Mar 17, 2019
Online Publication Date Mar 22, 2019
Publication Date Jun 30, 2019
Deposit Date Apr 2, 2019
Publicly Available Date Mar 23, 2020
Journal Digital Investigation
Print ISSN 1742-2876
Electronic ISSN 1742-2876
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 29
Pages 67-81
Keywords Video tampering; Video synthesis; Deep learning; Video forgery
Public URL


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