Digital videos are now low-cost, easy to capture and easy to share on social media due to the common feature of video recording in smart phones and digital devices. However, with the advancement of video editing tools, videos can be tampered (forged) easily for propaganda or to gain illegal advantages—ultimately, the authenticity of videos shared on social media cannot be taken for granted. Over the years, significant research has been devoted to developing new techniques for detecting different types of video tampering. In this paper, we offer a detailed review of existing passive video tampering detection techniques in a systematic way. The answers to research questions prepared for this study are also elaborated. The state-of-the-art research work is analyzed extensively, highlighting the pros and cons and commonly used datasets. Limitations of existing video forensic algorithms are discussed, and we conclude with research challenges and future directions.

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Digital Video Tampering Detection and Localization: Review, Representations, Challenges and Algorithm

In the legal world, a evidence of video recording has a high level of confidence and is often the evidence. This raises the issue of ensuring an original video or engineering result in digital evidence in the form of forensic video on storage data. One form of engineering that can be done on the video is tampering. The technique used is zooming, Laplacian sharpening, contrast brightness, optical deblurring, turbulence deblurring, exposure, temperature tint, Wiener filter, bilateral filter, unsharp masking and homomorphic filter. The result shows that the accuracy of the assessment in case of CCTV video case analysis (CCTV) about car bumping and car tire fitting according to DI Yogyakarta Police investigators who identified in the field of INAFIS said that 70% of identification of CCTV video footage and face of the perpetrator or suspect or attacker already can be processed investigation of evidence.

Analysis of Forensic Video in Storage Data Using Tampering Method

Video forgery detection

The use of digital videos in criminal investigation and civil litigation has become popular, this is due to the advancement of embedded cameras in handheld devices such as mobile phones, PDA’s and tablets. However, the content of digital videos can be extracted, enhanced and modified using inexpensive and user friendly video editing software, such as; Adobe Photoshop, Sefexa, etc. Thus, the influx of these video editing softwarelead to the creation of serious problems that are associated with the authenticity of digital videos by making their validity questionable. In order to address these problems, two approaches for the authentication of digital videos were proposed by digital forensic researchers. The approaches are either active or passive. Active approaches are the earliest form of video authentication techniques; an active approach is based on digital watermark technology that is used for video authentication and ownership verification. A digital watermark is a hidden digital marker embedded in a noise tolerant video signal. However, the problem with the active approach to video authentication is that it can only be applied in limited situations and it requires the use of a special hardware. Moreover, an authorized person responsible for the watermark insertion can tamper with the video before inserting the digital watermark. Furthermore, techniques for encryption can be used to prevent an unauthorized person from tampering with the content of the video, however, these encryption techniques donot prevent the file owner from tampering with his own video. This limits the ability of digital watermark to ensure authenticity in digital videos. In response to these limitations, passive approaches were introduced. Passive approaches rely on the behaviour of features embedded in a video for forgery detection purposes. Thus, the aim of this doctoral study as a contribution to the field of digital forensic is to develop techniques based on selected video features that can be used to detect tampering of a digital video. In this study, passive forensic techniques are proposed to detect (1) Digital video inpainting forgery, and (2) Chroma key forgery in digital videos. Each of these techniques focus on the specific features that can be used to detect that kind of forgery. Firstly, a technique for the detection of video inpainting forgery is proposed using the statistical correlation of hessian matrix features extracted from the suspected video. Secondly, another technique is proposed for the detection of chroma key forgery in a digital video using the statistical correlation of blurring features extracted from the suspected video. Results from these experiments conducted have proven that hessian matrix features can effectively be used to detect video inpainting forgery with 99.79% accuracy whilst the blurring feature can effectively detect chroma key forgery in digital videos with 99.12% accuracy.

Bagiwa Aminu Mustapha

Papers

Passive video forgery detection using frame correlation statistical features / Aminu Mustapha Bagiwa