Camera identification based on very low bit rate videos with overall noise pattern having time varying statistics
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This paper proposes a method for performing the camera identification based on very low bit rate videos with the overall noise patterns having time varying statistics. First, the overall noise pattern of each frame of each video is converted to a vector. Then, the odd order statistic moments of these vectors are computed. By performing the principal component analysis, only the most major component of each statistic moment vector is attained. These components of all the frames form a feature vector for each video. To minimize the intraclass separation and maximize the interclass separation, the linear discriminant analysis is performed. As many eigenvalues of the interclass separation matrix are close to zero, the column vectors which span the null spaces of the corresponding matrices are found and the feature vector of each video is projected to these columns and forms a new vector. It is found that these new vectors are pairwisely linear separable. Hence, a bank of perceptrons can be applied to perform the camera identification. Computer numerical simulations show that our proposed method significantly outperforms the conventional correlation based method and the conventional support vector machine based method.
KeywordsVery low bit rate videos Overall noise patterns having time varying statistics Video forensics Camera identification Statistic moments Principal component analysis Linear discriminant analysis Null space approach Pairwisely linear separable Bank of perceptrons
This work was supported partly by the National Nature Science Foundation of China (No. 61372173), the Guangdong Higher Education Engineering Technology Research Center for Big Data on Manufacturing Knowledge Patent (No. 501130144), the Hundred People Plan from the Guangdong University of Technology and the Young Thousand People Plan from the Ministry of Education of China.
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