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Related Experiment Video

Updated: Oct 22, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Detecting and Locating Passive Video Forgery Based on Low Computational Complexity Third-Order Tensor Representation.

Yasmin M Alsakar1, Nagham E Mekky1, Noha A Hikal1

  • 1Department of Information Technology, Faculty of Computers and Information Science, Mansoura University, Mansoura 35516, Egypt.

Journal of Imaging
|August 30, 2021
PubMed
Summary

This study introduces a novel passive video forgery detection scheme using tensor data. The method accurately detects and locates video insertions and deletions with high precision and efficiency.

Keywords:
GLCMHarrisSVDTensorcorrelationdigital forensicsinter-frame forgeryvideo forensic

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Area of Science:

  • Computer Science
  • Digital Forensics
  • Image Processing

Background:

  • Video forgeries are prevalent due to widespread social media sharing and accessible editing software.
  • Video integrity is crucial for evidence in legal and security contexts.
  • Existing methods struggle with diverse video content and forgery types.

Purpose of the Study:

  • To develop a passive video forgery detection scheme.
  • To accurately detect and locate video insertion and deletion forgeries.
  • To achieve high precision with low computational complexity.

Main Methods:

  • Utilizing a third-order tensor tube-fiber mode to represent video data.
  • Employing core tensors for forgery detection and localization.
  • Applying orthogonal transformation for data reduction and feature extraction.

Main Results:

  • Achieved up to 99% precision in detecting and locating insertion/deletion forgeries.
  • Demonstrated effectiveness on static, dynamic, and complex video datasets (zooming, quick motion).
  • Showcased reduced processing time (35s for 40 forged frames) and linear computational complexity.

Conclusions:

  • The proposed tensor-based scheme offers a superior solution for passive video forgery detection.
  • The method is robust across various challenging video scenarios.
  • It provides an efficient and accurate tool for verifying video authenticity.