A Video Dataset for an Efficient Camcording Attack Evaluation

Asma Kerbiche, Saoussen Ben Jabra, Ezzeddine Zagrouba, Vincent Charvillat

2018

Abstract

Any video watermarking scheme dedicated to copyright protection should be robust against several attacks and especially against malicious and dangerous attacks such as camcording. Indeed, this attack has become a real problem for cinematographic production companies. However, until now the researchers don't evaluate the robustness of their video watermarking approaches against this attack or they consider it as a combination of some usual attacks. To resolve this problem, several studies proposed camcording simulators which encourage and help researchers in video watermarking domain to include the camcording in the robustness evaluation. In this paper, a dataset of camcorder videos dedicated to an efficient robustness evaluation of watermarking schemes is proposed which can help researches on camcording simulators' creation. In this dataset, videos are captured in realistic scenarios in the cinema and are recorded using five capture devices and from four positions. In more, the proposed dataset contains marked versions of the proposed videos using three different video watermarking techniques. This allows researchers comparing their approaches with these techniques. Experimental results show that the robustness evaluation based on the proposed dataset is more efficient than simulators based evaluation thanks to the diversity of the used capturing devices and the real conditions of videos recording.

Paper Citation

in Harvard Style

Kerbiche A., Ben Jabra S., Zagrouba E. and Charvillat V. (2018). A Video Dataset for an Efficient Camcording Attack Evaluation. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP; ISBN 978-989-758-290-5, SciTePress, pages 150-158. DOI: 10.5220/0006617201500158

in Bibtex Style

@conference{visapp18,
author={Asma Kerbiche and Saoussen Ben Jabra and Ezzeddine Zagrouba and Vincent Charvillat},
title={A Video Dataset for an Efficient Camcording Attack Evaluation},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP},
year={2018},
pages={150-158},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006617201500158},
isbn={978-989-758-290-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP
TI - A Video Dataset for an Efficient Camcording Attack Evaluation
SN - 978-989-758-290-5
AU - Kerbiche A.
AU - Ben Jabra S.
AU - Zagrouba E.
AU - Charvillat V.
PY - 2018
SP - 150
EP - 158
DO - 10.5220/0006617201500158
PB - SciTePress