Pawar Nilesh


In the current times the level of video forgery has increased on the internet with the increase in the role of malware that has made it possible for any user to upload, download and share objects online including audio, images, and video. Specifically, Video Editor and Adobe Photoshop are some of the multimedia software and tools that are used to edit or tamper medial files. Added to this, manipulation of video sequence in a way that objects within the frame are inserted or deleted are among the common malicious video forgery operations. In the present study, literature concerning video forgery is reviewed primarily those that use several video forgery detection in the form of passive blind method on three types of forgery namely cloning forgery, source cameral identification and splice forgery. The present study employed a video authentication method that detects and determines both region duplication and frame duplication in terms of video forgery, and locates factors that impact video forgery. In the present study, video processing into sub-blocks and the moments geometric features for every macro-block were extracted. This led to the enhanced accuracy of detection. Moreover, the optimum sorting algorithm led to minimized computational time taking account number of blocks and features numbers into consideration.


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