Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Exposing digital forgeries in video by detecting double MPEG compression
MM&Sec '06 Proceedings of the 8th workshop on Multimedia and security
Exposing digital video forgery by ghost shadow artifact
MiFor '09 Proceedings of the First ACM workshop on Multimedia in forensics
Exposing Digital Forgeries in Interlaced and Deinterlaced Video
IEEE Transactions on Information Forensics and Security - Part 1
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As screenshots of copyrighted video contents are spreading through the Internet without any regulation, the copyright infringement arises gradually. However, there are still no techniques that prevent illegal screenshots from spreading out. Since most television systems use interlaced scanning mode and camcorders offer interlaced recording, many screenshots are interlaced. In this paper, we propose a screenshot identification scheme using unique characteristic of interlaced video, combing artifact. To do this, several blocks of the input image are selected for the significant combing artifact they have. In each block, eight features that represent the artifact are extracted. These extracted features are applied to train and test support vector machine for identifying whether the input image is a screenshot or not. In the experiments, the identification accuracy is measured when the input image is converted from and to common multimedia formats. The results prove that the proposed identification scheme performs well under the widely used image formats as JPEG, BMP, TIFF and video formats as MPEG-2, MPEG-4, H.264.