LAUSR

It is a generic belief that digital video can be proffered as visual evidence in areas like politics, criminal litigation, journalism and military intelligence services. Multicamera smartphones with megapixels of resolution are a common hand-held device used by everyone. This has made the task of video recording very easy. At the same time a variety of applications available on smart phones have made this indispensable source of information vulnerable to deliberate manipulations. Hence, content authentication of video evidence becomes essential. Copy-move forgery or Copy-paste forgery is consequential forgery done by forgers for changing the basic understanding of the scene. Removal or addition of frames in a video clip can also be managed by advanced apps on smartphones. In case of surveillance, the video camera and the background are stable which makes forgery easy and imperceptible. Therefore, accurate Video forgery detection is crucial. This paper proposes an efficient method—VFDHSOG based on Histograms of the second order gradient to locate ‘suspicious’ frames and then localize the CMF within the frame. A ‘suspicious’ frame is located by computing correlation coefficients of the HSOG feature after obtaining a binary image of a frame. Performance evaluation is done using the benchmark datasets Surrey university library for forensic analysis (SULFA), the Video tampering dataset (VTD) and SYSU-OBJFORGED dataset. SULFA has video files of different quality like q10, q20 etc., which represents high compression. The VTD dataset provides both, i.e. inter and intra frame forgery. The SYSU dataset covers different attacks like scaling and rotation. An overall accuracy of 92.26% is achieved with the capability to identify attacks like scale up/down and rotation.