Feature extraction and machine learning solutions for detecting motion vector data embedding in HEVC videos
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Feature extraction and machine learning solutions for detecting motion vector data embedding in HEVC videos Tamer Shanableh 1 Received: 3 June 2020 / Revised: 3 August 2020 / Accepted: 2 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
This paper proposed three feature extraction solutions suitable for detecting data embedding in motion vectors (MVs) of coded HEVC videos. In the first feature extraction solution, videos under consideration are reencoded and features are extracted from both videos from Coding Units (CUs). The difference between the two feature sets form a feature vector at a CU level. The CU level feature vectors are then summarized by computing the average and standard deviation of individual features. This summarization is computed at a frame-level and at a video sequence level. Detection models are then used to detect MV data embedding. To generate the detection models, HEVC videos are reencoded whilst employing two different data embedding solutions. Feature variables are then computed and the detection models at frame and sequence levels are generated. The second solution is an extension of an existing work that uses the concept of MV consistency for computing feature variables. In this work, we extent the MV consistency concept to HEVC coded videos by grouping sub CUs based on their coding depths. One set of features is computed by finding the joint probability that a CU has a given coding depth and the bitrate of the MV differences of the sub CUs are smaller than or equal to the between-CU MV differences. Another set of features is computed by finding a similar probability but for sub CUs with MV differences greater than the between-CU MV differences. The third solution combines the features of all of the aforementioned solutions resulting in a set of 16 feature variables. The feature variables are visualized by projecting them using spectral regression where it is shown that the third solution results in separable features. In comparison to existing work, experimental results show excellent classification accuracies for HEVC videos coded at different spatiotemporal resolutions and different bitrates. Keywords Data embedding . Machine learning . Steganalysis . Steganography . Video coding
* Tamer Shanableh [email protected]
1
Department of Computer Science and Engineering, American University of Sharjah, Sharjah, United Arab Emirates
Multimedia Tools and Applications
1 Introduction Data embedding in digital videos has a number of important applications such as digital rights management, quality assessment and error correction of streamed visual content and convert conversations. In general, data embedding in compressed video can be performed by modifying values of Motion Vectors (MVs), quantization parameters, prediction modes, block partitioning decisions, Discrete Cosine Transform (DCT) coefficients and variable length codes. The work reported in [1] is an example of embedding data in compressed video by modifying the values of its motion
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