Residual-Based Forensic Comparison Of Video Sequences

Video content can be acquired with off-the-shelf hardware, and is thus increasingly used to record events. With the growing role of video data for communicating to a large audience, we need tools to ensure the authenticity of video content. However, until now, only few methods exist to forensically analyze videos.In this work, we propose a method for statistically comparing two video sequences. Per sequence, intra- and inter-frame residuals are computed. Optical flow is used to compensate for motion artifacts on inter-frame residuals. We use one sequence to build a statistical model, and compare it to the second sequence. From a forensic perspective, the proposed method enables two applications. First, manipulations can be accurately localized if both sequences are subsequences of the same video. Second, source cameras can be distinguished if both sequences stem from different videos. The proposed method is evaluated on collected smartphone data and green screen splices. Further, it is quantitatively compared to both a recent PRNU-based approach and a technique based on autoencoders.