Spatio-Temporal Rich Model-Based Video Steganalysis on Cross Sections of Motion Vector Planes
- Submitting institution
-
University of Derby
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 785317-4
- Type
- D - Journal article
- DOI
-
10.1109/TIP.2016.2567073
- Title of journal
- IEEE Transactions on Image Processing
- Article number
- -
- First page
- 3316
- Volume
- 25
- Issue
- 7
- ISSN
- 1057-7149
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2016
- URL
-
http://ieeexplore.ieee.org/document/7468453/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
-
- Citation count
- 17
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper was one of the pioneer studies in video steganalysis problem, helping reveal covert communication channels in videos. The developed method was tested in a quite large video dataset and the results outperformed the counterparts. The research findings were used to obtain the RAEng Industrial Fellowship award (ISS1617-45) with Titan IC Systems Ltd, which provides SoC for content inspection. Kurugollu took the research developed in this paper further into Deep Packet Inspection systems. This helped Titan IC Systems to diversify its product portfolio, facilitating a successful acquisition by NVIDIA, which is the world leader in SoC and GPU.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
