Abstract
To detect frame duplication in degraded videos, we proposed a coarse-to-fine approach based on locality-sensitive hashing and image registration. The proposed method consists of a coarse matching stage and a duplication verification step. In the coarse matching stage, visually similar frame sequences are preclustered by locality-sensitive hashing and considered as potential duplication candidates. These candidates are further checked by a duplication verification step. Being different from the existing methods, our duplication verification does not rely on a fixed distance (or correlation) threshold to judge whether two frames are identical. We resorted to image registration, which is intrinsically a global optimal matching process, to determine whether two frames coincide with each other. We integrated the stability information into the registration objective function to make the registration process more robust for degraded videos. To test the performance of the proposed method, we created a dataset, which consists of 3 subsets of different kinds of degradation and 117 forged videos in total. The experimental results show that our method outperforms state-of-the-art methods for most cases in our dataset and exhibits outstanding robustness under different conditions. Thanks to the coarse-to-fine strategy, the running time of the proposed method is also quite competitive.
Highlights
With various nonlinear editing tools such as Adobe Premiere, Microsoft Movie Maker, and Sony Vegas, it is much easier for people to tamper the content of a video
We proposed a new method for frame duplication detection, for the degraded videos
In the coarse matching stage, we use locality-sensitive hashing to precluster the visually similar subsequences. rough coarse matching, the total number of subsequences which need finer duplication verification can be reduced by several orders of magnitude. e duplication verification step exploits image registration to identify the identical subsequences
Summary
With various nonlinear editing tools such as Adobe Premiere, Microsoft Movie Maker, and Sony Vegas, it is much easier for people to tamper the content of a video. Among different approaches to video forgery, frame duplication, which copies a sequence of frames to another position in the timeline, may be one of the most convenient yet effective means to hide or counterfeit events. Since the source and target frames simultaneously exist in the video, frame duplication forgery can be exposed by detecting abnormal identical frame sequences. On this basis, several methods have been proposed [4,5,6,7,8]. Ey extract features from the frames and set the distance threshold between the features Such methodology makes it difficult for these methods to perform robustly when applied in realistic frame duplication detection (FDD), where degradation is quite common. E rest of this paper is organized as follows: in section “Related Work,” we briefly introduce related work, and in section “Proposed Method,” the proposed method is detailed. e experimental results are presented in section “Experimental Results.” e conclusion and future directions are drawn in section “Conclusion and Future work.”
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