Video interframe forgery detection: Classification, technique & new dataset

Abstract

Multimedia communication as well as other related innovations are gaining tremendous growth in the modern technological era. Even though digital content has traditionally proved to be a piece of legitimate evidence. But the latest technologies have lessened this trust, as a variety of video editing tools have been developed to modify the original video. Therefore, in order to resolve this problem, a new technique has been proposed for the detection of duplicate video sequences. The present paper utilizes gray values to extract Hu moment features in the current frame. These features are further used for classification of video as authentic or forged. Afterwards there was also need to validate the proposed technique using training and test dataset. But the scarcity of training and test datasets, however, is indeed one of the key problems to validate the effectiveness of video tampering detection techniques. In this perspective, the Video Forensics Library for Frame Duplication (VLFD) dataset has been introduced for frame duplication detection purposes. The proposed dataset is made of 210 native videos, in Ultra-HD and Full-HD resolution, captured with different cameras. Every video is 6 to 15 seconds in length and runs at 30 frames per second. All the recordings have been acquired in three different scenarios (indoor, outdoor, nature) and in landscape mode(s). VLFD includes both authentic and manipulated video files. This dataset has been created as an initial repository for manipulated video and enhanced with new features and new techniques in future.