Vol.35, No.6, 2020, pp.513-521, doi:10.32604/csse.2020.35.513
OPEN ACCESS
ARTICLE
Video Source Identification Algorithm Based on 3D Geometric Transformation
  • Jian Li1, Yang Lv1, Bin Ma1,*, Meihong Yang2, Chunpeng Wang1, Yang Zheng3
1 Qilu University of Technology (Shandong Academy of Sciences), Shandong Provincial Key Laboratory of Computer Networks, Jinan, 250353, China
2 Shandong Computer Science Center, Shandong Provincial Key Laboratory of Computer Networks, Jinan, 250014, China
3 University of Connecticut, Mansfield, Connecticut, 06269, USA
* Corresponding Author: Bin Ma. Email:
Abstract
Digital video has become one of the most preferred ways for people to share information. Considering people tend to release illegal information in anonymous way, the problem of video source identification attracts more and more attention as an important part of multimedia forensics. The Photo-Response Non-Uniformity (PRNU) based algorithm shows to be a promising solution for the problem of video source identification. However, it is necessary to make a geometric transformation for testing PRNU noise to align it with the reference noise, due to the effect of video stabilization. This paper analyzes the three-dimensional (3D) characteristics of camera jitters and studies how to estimate the parameters of 3D geometric transformation when aligning PRNU noises between reference and test. In the algorithm design, quaternion is used to transform PRNU noise image in 3D space, and 15 rotation axes of 3D space are estimated for experiments. 162 videos of 9 smart phones were tested. Most of the videos got a higher peak to correlation energy (PCE) value by using this algorithm, and showed better results when applied to videos with complex texture. The experiment part also records the geometric transformation parameters of the PRNU noise which need to map from image domain to video domain.
Keywords
Video forensics; Photo-Response Non-Uniformity (PRNU) noise; video source identification; video stabilization
Cite This Article
. , "Video source identification algorithm based on 3d geometric transformation," Computer Systems Science and Engineering, vol. 35, no.6, pp. 513–521, 2020.
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