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Using similarity analysis to detect frame duplication forgery in videos

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Abstract

Duplication of selected frames from a video to another location in the same video is one of the most common methods of video forgery. However, few algorithms have been suggested for detecting this tampering operation. This paper proposes an effective similarity-analysis-based method for frame duplication detection that is implemented in two stages. In the first stage, the features of each frame are obtained via SVD (Singular Value Decomposition). Next, the Euclidean distance is calculated between features of each frame and the reference frame. After dividing the video sequence into overlapping sub-sequences, the similarities between the sub-sequences are calculated, and then our algorithm identifies those video sequences with high similarity as candidate duplications. In the second stage, the candidate duplications are confirmed through random block matching. The experimental results show that our algorithm provides detection accuracy that is higher than the previous algorithms, and it has an outstanding performance in terms of time efficiency.

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Acknowledgments

This work was supported by the Industry-University Cooperation Major Projects in Fujian Province (Grant No. 2012H6006), the Program for New Century Excellent Talents in University in Fujian Province (Grant No. JAI1038), the University Services HaiXi Major Project in Fujian Province (Grant No. 2008HX200941-4-5), the Science and Technology Department of Fujian province K-class Foundation Project (Grant No. JA10064), and The Education Department of Fujian Province A-class Foundation Project (Grant No. JA10064).

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  1. School of Mathematics and Computer Science, Fujian Normal University, Fuzhou, China

    Jianmei Yang, Tianqiang Huang & Lichao Su

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  1. Jianmei Yang
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  2. Tianqiang Huang
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  3. Lichao Su
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Correspondence to Tianqiang Huang.

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Yang, J., Huang, T. & Su, L. Using similarity analysis to detect frame duplication forgery in videos. Multimed Tools Appl 75, 1793–1811 (2016). https://doi.org/10.1007/s11042-014-2374-7

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  • DOI: https://doi.org/10.1007/s11042-014-2374-7

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