An ensemble approach to detect copy-move forgery in videos

In the recent past, video forgery has increased rapidly due to the easy availability of the required tools to accomplish that. Temporal copy-move or frame duplication is one of the most common video forgery methods in which a set of consecutive frames is copied somewhere in the same video. This work proposes an ensemble based method to detect duplicate frames from a video. In this method, first, the frames are pre-processed and then three different kinds of features - Haralick, custom Haralick and Local Binary Pattern (LBP) are extracted from each frame of a video. Next, lexicographical sorting is performed to arrange the frames having similar feature values consecutively. A filter method is applied to eliminate the false detection and it also creates a duplicate sequence for each category. In the end, a voting mechanism is employed to predict the duplicate frames, if they exist. The proposed method performs very well (detection accuracy is 99.32%, the true positive rate is 99.79%, and the true negative rate is 99.19%) over an in-house dataset containing 300 videos of seven different types. The robustness of the proposed method has also been tested by adding various postprocessing to the video samples. The proposed method outperforms many state-of-the-art methods used here for comparison.