LAUSR

In recent decades, frame duplication is a common inter frame tampering operation in the digital videos. To find the duplicate frames with better computational time, multi scale local oriented feature descriptors are proposed in this paper. Initially, histogram equalization is used to improve the visual quality of the images or videos, which are collected from surrey university library for forensic analysis dataset. Then, feature extraction is accomplished utilizing binary robust invariant scalable keypoint, speeded up robust features, and maximally stable extremal regions to extract feature vectors or key points from the enhanced images. After identifying the keypoints, matched keypoints are evaluated by hamming distance and k-means clustering algorithm from the source and moving video frames. The multi scale local oriented feature descriptors with two step feature matching significantly decreases the computational time and effectively determines forged and non-forged frames in the video sequences. Simulation results showed that the proposed model achieved better performance in passive video forgery detection in terms of accuracy, sensitivity, fscore and specificity. Compared to the existing approaches like spatio temporal context learning, inter-frame forgery detection algorithm and adaptive parameter-based visual background extractor algorithm, the proposed model obtained better detection accuracy of 95.10%, and average detection time of 1.04 seconds per frame.