Multiple forgery detection in video using inter-frame correlation distance with dual-threshold

Video forgery can be defined as the modification of the video contents. The alteration of the video by deletion and modification in the sequence of frames is a trivial task, which has made the authentication and originality detection more important. Frame insertion and deletion are the most common type of video forgery. The proposed method can identify these types of forgery along with its forged location, which makes this unique method. It defines the relationship between the adjacent frames using the correlation coefficient, finds the inter-frame correlation distance between the frames, calculates the minimum distance score, statistical features, and computes upper-bound, lower-bound threshold and sigma coefficient for the identification of forgery location. The proposed method defines insertion and deletion type forgery by using threshold controlled parameters and it is validated on the VIFFD dataset. The proposed method has also identified forgery with 97% accuracy at the frame level and 83% accuracy at the video level. The result analysis shows the superiority of the proposed method over the existing methods. This method is very effective in identifying the forgery type with its frame location.