Intra-frame Copy-move Video Forgery Detection

With the increase in sharing of videos worldwide over social networks, presence of high-quality fakes is on increase. Forged videos affect the authenticity and integrity of the video as a whole. This can lead to serious implications. For example, in case of video to be used in courts as an evidence, presence of forgery can implicate innocents or help criminal to escape justice. This calls for the detection mechanisms to counter. This leads to the discovery of several different approaches to detect copy-move forgery by analysing the side effects due to tempering. One of the most common approaches is copy-move video forgery which consists of duplicating area of frame. Traditional approach detects for patterns related to duplication manually which is not so successful. In contrast, methods related to deep learning gives better results. Therefore, this research follows deep learning model using pertained architecture to detect copy-move video forgery.