Motion-Adaptive Detection of HEVC Double Compression with the Same Coding Parameters

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

1 Scopus Citations
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Author(s)

  • Qiang Xu
  • Xinghao Jiang
  • Tanfeng Sun
  • Alex C. Kot

Detail(s)

Original languageEnglish
Pages (from-to)2015-2029
Journal / PublicationIEEE Transactions on Information Forensics and Security
Volume17
Online published18 May 2022
Publication statusPublished - 2022
Externally publishedYes

Abstract

High Efficiency Video Coding (HEVC) double compression detection is of prime significance in video forensics. However, double compression with the same parameters and video content with high motion displacement intensity have become two main factors that limit the performance of existing algorithms. To address these issues, a novel motion-adaptive algorithm is proposed in this paper. Firstly, the analysis of GOP structure in HEVC standard and the coding process of HEVC double compression are provided. Next, sub-features composed of fluctuation intensities of intra prediction modes and unstable Prediction Units (PUs) in normal Intra-Frames (I-frames) and optical flow in adaptive I-frames are exploited in our algorithm. Each sub-feature is extracted during the process of multiple decompression. We further combine these sub-features into a 27-dimensional detection feature, which is finally fed to the Support Vector Machine (SVM) classifier. By following a separation-fusion detection strategy, the experimental result shows that the proposed algorithm outperforms the existing state-of-the-art methods and demonstrates superior robustness to various motion displacement intensities and a wide variety of coding parameter settings.

Research Area(s)

  • Detection algorithms, double compression detection, Encoding, Feature extraction, HEVC, intra prediction modes, motion displacement intensity, optical flow, Partitioning algorithms, Prediction algorithms, Standards, unstable prediction units, Video sequences