Abstract
Frame deletion detection is an arrestive field for video forensics in recent years. This paper presents a frame deletion detection algorithm based on optical flow orientation variation. Optical flow field has been proved to be effective for the detection of the frame deletion operation. However, current optical-flow based methods have relatively poor detection performance for real-world videos. Our research aims to develop a new forensic method to detect frame deletion for real-world videos. The proposed algorithm firstly develops an analytical model for flow orientation variation between adjacent optical flow fields, and exhibits a novel forensic clue for frame deletion detection, under an ideal but reasonable assumption. Accordingly, an effective descriptor, namely pseudo flow orientation variation (PFOV), is created to approximate the flow orientation variation, as a discriminative description feature to capture the frame deletion trace. The Frobenius norm and a smooth function are also introduced to quantify each descriptor to form an one-dimensional time series, and further statistics technique is adopted to detect the anomalous points in the time series caused by frame deletion. In addition, to improve the descriptor performance, the Robust Principal Component Analysis (RPCA) is applied to extract moving objects from video sequences and compute the proposed descriptor on them. The tests are made on 324 real-world videos, and experiment results demonstrate that the true positive rate can reach 90.12%, meanwhile the false alarm rate is 7.71%, which indicates the superiority of the proposed method in the forensic algorithms for real-world video.
Highlights
With the rapid development of communication transmission techniques, multimedia becomes the mainstream medium to gain and exchange information
In order to verify the validity, we show some visual presentations of the descriptor to demonstrate that the inconsistency produced by frame deletion (FD) can be captured by the flow orientation variation
In this paper, we propose a frame deletion detection approach for real-world videos based on the inconsistency of the optical flow orientation variation
Summary
With the rapid development of communication transmission techniques, multimedia becomes the mainstream medium to gain and exchange information. The first forensic clue for FD detection was explored by Wang and Farid in [2], based on a strong assumption They assumed that after forgery operation was applied to a video, it would be accompanied with recompression of the video. Methods relying on extracting features from the compression videos make FD detection still in its infancy These methods are on the basis of a strong assumption, whereas the recompression is not always performed for videos with forgery manipulation. Various coding schemes are alternative for videos to be recompressed, while most of the algorithms are only suitable for specific compression strategies, which are lack of robustness to different coding schemes In consideration of these limitations, researchers devote to looking for forensic clues from the video content, and based on these content-based methods, a novel FD detection approach is proposed in this paper.
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