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Forensic approach for detecting the region of Copy-Create video forgery by applying frame similarity approach

Author Affiliations

  • 1Department of Computer Applications, Basaveshwar Engineering College, Bagalkot, India
  • 2Department of Computer Science & Engineering, Maharaja Institute of Technology, Mysore, India
  • 3Department of Computer science & Engineering, KLE Institute of Technology, Hubli, India

Res. J. Computer & IT Sci., Volume 7, Issue (2), Pages 12-17, December,20 (2019)


Now a day′s videos are living in the heart of the modern communication world. Every one socio living being in this world uses videos are parts of social media like Whats-app, Facebook, Instagram, and Twitter. However, the problem persists the trustworthiness of those video published in the media world. Due to high-end open source free video editing software are readily available for editing the source video and modifying the content of the video becomes simpler. In forensically a part of media information copied and pasted in the same or another footage without changing the source of information is called it as copy-create forgery techniques. At presently some researcher found the methods in both active and passive forgery techniques those are all focusing on hardware embedded and high processing detection with the lowest accuracy and executed by considering minimal parameters which are becoming a bottleneck for the unique solution. Now we are proposing techniques with the help of necessary video vision processing to identify the forged region with extracting necessary information and applying the backtrack methods for investigation method to detect the forged part and authenticating the source of the video. We are proposing concepts and implementation by considering the region of interest parameter by visualizing and analyzing the very basic pixel mapping along with block matching of a group of pictures converted by forged video along with source information. We are taking the statistical mean frame of each forged frame along with color channels and deducing and mapping with each block and generating a forged region of copy-create forged video. We are using forensically standard forgery data set created by Surrey University as SULPA and its parser dataset REWIND with customizing with the help of visionary parameter for testing the result. We succeeded 96% for accuracy and precession of the result. We also got the excellent accuracy in other standard dataset YTD and SYSU-OBJ-FORGE dataset.


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