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Summary

This study introduces a new method for detecting double compression in H.265/HEVC videos, focusing on B-frames. The approach achieves 80.06% accuracy, offering a practical solution for identifying recompressed video content.

Keywords:
B-framesH.265/HEVCdouble compressionvideo forgery

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Area of Science:

  • Digital video compression and processing
  • Machine learning for video analysis
  • Digital forensics and media integrity

Background:

  • Double video compression is a common issue, degrading video quality and posing challenges for forensic analysis.
  • Existing detection methods often overlook H.265/HEVC videos with B-frames, a prevalent scenario in modern video streaming.
  • Temporal inconsistencies introduced by recompression are key indicators of double compression.

Purpose of the Study:

  • To propose and evaluate a novel method for detecting double compression in H.265/HEVC videos, specifically addressing the under-researched area of B-frame inclusion.
  • To develop a robust feature extraction and classification framework capable of identifying recompressed video content accurately.
  • To assess the practical applicability of the proposed method in realistic double compression scenarios.

Main Methods:

  • Extraction of frame-level encoding features, including frame type, coding unit (CU) size, quantization parameter (QP), and prediction modes.
  • Representation of each video as a 28-dimensional feature vector.
  • Training a bidirectional Long Short-Term Memory (Bi-LSTM) classifier to model temporal inconsistencies arising from recompression.

Main Results:

  • The proposed method achieved a detection accuracy of 80.06% on a custom dataset of 129 HEVC-encoded videos.
  • The approach demonstrated superior performance compared to two existing baseline methods.
  • The results indicate the method's effectiveness in identifying double compression in realistic scenarios.

Conclusions:

  • The developed method provides an effective solution for detecting double compression in H.265/HEVC videos, even those containing B-frames.
  • The use of Bi-LSTM classifiers trained on specific encoding features shows promise for video forensic applications.
  • The study highlights the importance of addressing B-frame scenarios for comprehensive double compression detection.