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http://dx.doi.org/10.5909/JBE.2019.24.6.1134

Deep Learning based HEVC Double Compression Detection  

Uddin, Kutub (Korea Aerospace University, School of Electronics and Information Engineering)
Yang, Yoonmo (Korea Aerospace University, School of Electronics and Information Engineering)
Oh, Byung Tae (Korea Aerospace University, School of Electronics and Information Engineering)
Publication Information
Journal of Broadcast Engineering / v.24, no.6, 2019 , pp. 1134-1142 More about this Journal
Abstract
Detection of double compression is one of the most efficient ways of remarking the validity of videos. Many methods have been introduced to detect HEVC double compression with different coding parameters. However, HEVC double compression detection under the same coding environments is still a challenging task in video forensic. In this paper, we introduce a novel method based on the frame partitioning information in intra prediction mode for detecting double compression in with the same coding environments. We propose to extract statistical feature and Deep Convolution Neural Network (DCNN) feature from the difference of partitioning picture including Coding Unit (CU) and Transform Unit (TU) information. Finally, a softmax layer is integrated to perform the classification of the videos into single and double compression by combing the statistical and the DCNN features. Experimental results show the effectiveness of the statistical and the DCNN features with an average accuracy of 87.5% for WVGA and 84.1% for HD dataset.
Keywords
Video Forensic; Double Compression Detection; HEVC; Picture Partitioning; Deep Learning;
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