Double compression detection in HEVC-coded video with the same coding parameters using picture partitioning information

Detection of double compression, particularly in the high-efficiency video coding (HEVC) compressed domain, is one of the most operative and efficacious ways of authenticating the validity of videos in the field of forensic analysis. The strength of identifying abnormalities in the videos confides in diverse coding parameters (such as quantization parameters, size and structure of the group of pictures, and modes of compression). Many methods have been introduced to dig up HEVC double compression with different coding parameters. However, the revelation of the HEVC double compression under the same coding environments still remains a competitive task, as recompressions leave small footprints. In this paper, we introduce a novel method based on frame partitioning information to distinguish between single and double compressions with the same coding parameters. We propose extracting statistical and deep convolution neural network (DCNN) features from partition pictures and prediction modes, including coding unit, prediction unit, transform unit, and most probable modes information. Finally, machine learning technology is integrated to categorize videos into two classes, single and double compressions, by combining the statistical and DCNN features. We obtain the best experimental results by assembling the statistical and DCNN features for wide video graphics array (WVGA) and high-definition (HD) sequences with average accuracies of 99.66% and 99.60% in all-intra and 99.46% and 99.33% in low-delay P modes respectively. Experimental results of the proposed system show the effectiveness and efficiency over the state-of-the-art techniques in video forensic. •This proposes the video forensic analysis in the HEVC-coded videos for ensuring the authenticity and integrity by detecting double compression.•Mainly focuses on the portioning and prediction information for discriminating HEVC single and double compressions.•This introduces two classes of features: statistical features and deep convolution neural network (DCNN) features for evaluating the proposed system.•Experiments are carried out in separate and combined fashion for statistical and DCNN features to show the robustness of each feature set.•The full analysis and comparisons of the quantitative results are provided.