Video tamper detection based on multi-scale mutual information

Video frame manipulation has become commonplace with the growing easy access to powerful computing abilities. One of the most common types of video frame tampers is the copy-paste tamper, wherein a region from a video frame is replaced with another region from the same frame. In order to improve the robustness of passive video tampering detection, we propose a content-based video similarity tamper passive blind detection algorithm based on multi-scale normalized mutual information which can implement video frame copy, frame insertion and frame deletion tamper detection. The detail implementation of the proposed algorithm consists of multi-scale content analysis, single-scale content similarity measure, multi-scale content similarity measure, and tampering positioning. Firstly, we get the scales of the visual content of the video frame using Gaussian pyramid transform; Secondly, to measure the similarity of single-scale visual content, we define adjacent normalized mutual information of two frames according to information theory; Thirdly, we construct the multi-scale normalized mutual information descriptors to achieve the multi-scale visual content similarity measure of adjacent two frames using a linear combination. Finally, we use the local outlier isolated factor detection algorithm to detect the position of the video tampering. Experimental results show that the proposed approach can not only detect the video frame tampering position of delete, copy, and insert effectively, but also can detect the tampering of different and homology video encoding formats. We obtain a feature detecting accuracy in excess of 93 % and detection rate of 96 % across post processing operations, and are able to detect the delete, copy, and insert regions with a high true positive rate and lower false positive rate than the existing time field tamper detection methods.