Screenshot identification by analysis of directional inequality of interlaced video
1 Department of Computer Science, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
2 Information Technology R&D Center, SK Telecom, 11 Euljiro 2-ga, Jung-gu, Seoul, Republic of Korea
3 Department of Computer Software Engineering, Kumoh National Institute of Technology, Sanho-ro 77, Gumi, Gyeongbuk, Republic of Korea
4 Department of Computer Science and Division of Web Science and Technology, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
EURASIP Journal on Image and Video Processing 2012, 2012:7 doi:10.1186/1687-5281-2012-7Published: 2 May 2012
As screenshots of copyrighted video content are spreading through the Internet without any regulation, cases of copyright infringement have been observed. Further, it is difficult to use existing forensic techniques for determining whether or not a given image was captured from a screen. Thus, we propose a screenshot identification scheme using the trace of screen capture. Since most television systems and camcorders use interlaced scanning, many screenshots are taken from interlaced videos. Consequently, these screenshots contain the trace of interlaced videos, combing artifacts. In this study, we identify a screenshot using the characteristics of combing artifacts that appear to be shaped like horizontal jagged noise and can be found around the edges. To identify a screenshot, the edge areas are extracted using the gray level co-occurrence matrix (GLCM). Then, the amount of combing artifacts is calculated in the extracted edge areas by using the similarity ratio (SR), the ratio of the horizontal noise to the vertical noise. By analyzing the directional inequality of noise components, the proposed scheme identifies the source of an input image. In the experiments conducted, the identification accuracy is measured in various environments. The results prove that the proposed identification scheme is stable and performs well.