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Video Authentication: An Intelligent Approach

Video Authentication: An Intelligent Approach

Saurabh Upadhyay, Shrikant Tiwari, Shalabh Parashar
Copyright: © 2018 |Pages: 36
ISBN13: 9781522538226|ISBN10: 1522538224|EISBN13: 9781522538233
DOI: 10.4018/978-1-5225-3822-6.ch045
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MLA

Upadhyay, Saurabh, et al. "Video Authentication: An Intelligent Approach." Digital Multimedia: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2018, pp. 928-963. https://doi.org/10.4018/978-1-5225-3822-6.ch045

APA

Upadhyay, S., Tiwari, S., & Parashar, S. (2018). Video Authentication: An Intelligent Approach. In I. Management Association (Ed.), Digital Multimedia: Concepts, Methodologies, Tools, and Applications (pp. 928-963). IGI Global. https://doi.org/10.4018/978-1-5225-3822-6.ch045

Chicago

Upadhyay, Saurabh, Shrikant Tiwari, and Shalabh Parashar. "Video Authentication: An Intelligent Approach." In Digital Multimedia: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 928-963. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-3822-6.ch045

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Abstract

With the growing innovations and emerging developments in sophisticated video editing technology, it is becoming highly desirable to assure the credibility and integrity of video information. Today digital videos are also increasingly transmitted over non-secure channels such as the Internet. Therefore, in surveillance, medical, and various other fields, video contents must be protected against attempts to manipulate them. Video authentication has gained much attention in recent years. However, many existing authentication techniques have their own advantages and obvious drawbacks. The authors propose a novel authentication technique that uses an intelligent approach for video authentication. This chapter presents an intelligent video authentication algorithm for raw videos using a support vector machine, which is a non-linear classifier, and its applications. It covers both kinds of tampering attacks, spatial and temporal. It uses a database of more than 2000 tampered and non-tampered videos and gives excellent results with 98.38% classification accuracy. The authors also discuss a vast diversity of tampering attacks, which can be possible for video sequences. Their algorithm gives good results for almost all kinds of tampering attacks.

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