Detection and localization of multiple inter-frame forgeries in digital videos.

Sanctity and integrity of digital videos are crucial for the diverse real-world applications. It has significant social and legal implications. The technological advancements are posing new challenges as the video processing software that are typically designed to enhance the visual content, can adversely spawn unauthentic and malicious data that can be potentially hazardous. Robust algorithms are therefore needed to counter the deleterious effects. In this paper, we propose a passive-blind approach to detect and localize multiple kinds of inter-frame forgeries in digital videos like frame insertion, deletion and duplication. The forensic artefacts are designed based on correlation inconsistencies between the histogram-similarity patterns of the adjacent texture-feature encoded video frames. For the empirical evaluation, the algorithm uses texture features such as Histogram of Oriented Gradients (HoG), uniform and rotation invariant Local Binary Pattern (LBP). A customized dataset of 1370 tampered videos is created using the benchmark SULFA dataset due to lack of standard video dataset with inter-frame forgeries. A supervised SVM classifier is trained to detect video tampering where extensive analysis based on different histogram-similarity metrics is carried out with the proposed approach that exhibits an overall accuracy 99%. Further, the proposed method localizes the position of tampered frames in the video. It highlights forged frames using Chebyshev's inequality in case of frame insertion and deletion attacks. A comparative analysis with state-of-the-art methods is also presented that exhibits good efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]