Local tampering detection in video sequences

Abstract—Video sequences are often believed to provide stronger forensic evidence than still images, e.g., when used in lawsuits. However, a wide set of powerful and easy-to-use video authoring tools is today available to anyone. Therefore, it is possible for an attacker to maliciously forge a video sequence, e.g., by removing or inserting an object in a scene. These forms of manipulation can be performed with different techniques. For example, a portion of the original video may be replaced by either a still image repeated in time or, in more complex cases, by a video sequence. Moreover, the attacker might use as source data either a spatio-temporal region of the same video, or a region taken from an external sequence. In this paper we present the analysis of the footprints left when tampering with a video sequence, and propose a detection algorithm that allows a forensic analyst to reveal video forgeries and localize them in the spatio-temporal domain. With respect to the state-of-the-art, the proposed method is completely unsupervised and proves to be robust to compression. The algorithm is validated against a dataset of forged videos available online. I