A Framework for Analyzing Motion Vector Entropy in H.265 Encoded Videos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


In the domain of digital forensics, efficient and reliable detection of hidden information in compressed video files remains a challenge. This paper introduces a dynamic framework for unmasking concealed data in videos compressed using the H.265 codec, a prevalent standard in video compression. The proposed framework is underpinned by a detailed analysis of motion vectors' entropy, a critical component responsible for describing how objects shift between video frames. Entropy, as a measure of randomness, is predicted to contain anomalies in cases where information is hidden in the video. This paper capitalizes on these anomalies by assessing the entropy of motion vectors, seeking to identify and isolate them as potential markers of hidden data. This research is meant to be a blueprint in the world of video steganalysis, detecting hidden information in H.265 compressed videos. Expected to contribute significantly to video security and digital forensics fields, we wish to prompt further research into the application of entropy analysis across diverse data forms.
Original languageEnglish
Title of host publicationProceedings of the 10th International Conference on Advanced Intelligent Systems and Informatics (AISI-2024)
Place of PublicationCham, Switzerland
Publication statusAccepted/In press - 25 Dec 2023
Event10th International Conference on Advanced Intelligent Systems and Informatics (AISI’24) - Cairo, Cairo, Egypt
Duration: 20 Jul 202422 Jul 2024

Publication series

NameLecture Notes on Data Engineering and Communications Technologies
ISSN (Print)2367-4512
ISSN (Electronic)2367-4520


Conference10th International Conference on Advanced Intelligent Systems and Informatics (AISI’24)
Abbreviated titleAISI’24

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