A Robust Deep Learning-Based Video Watermarking Using Mosaic Generation

Souha Mansour, Saoussen Ben Jabra, Ezzedine Zagrouba

2023

Abstract

Recently, digital watermarking has benefited from the rise of deep learning and machine learning approaches. Even while effective deep learning-based watermarking techniques have been proposed for images, video still introduces extra difficulties, such as motion, temporal consistency, and spatial location. In this paper, a robust and imperceptible deep-learning-based video watermarking method based on CNN architecture and mosaic generation is suggested. The proposed approach is decomposed into two main steps: mosaic generation and signature embedding. This last one includes four stages: pre-processing networks for both the obtained mosaic and the watermark, embedding network, attack simulation, and extraction network. In fact, the main purpose of mosaic generation is to create an image from the original video and to provide robustness against malicious attacks, particularly against collusion attacks. CNN architecture is used to embed signature to maximize invisibility and robustness compromise. The proposed solution outperforms both traditional video watermarking and deep learning video watermarking, according to experimental evaluations on a variety of distortions.

Paper Citation

in Harvard Style

Mansour S., Ben Jabra S. and Zagrouba E. (2023). A Robust Deep Learning-Based Video Watermarking Using Mosaic Generation. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 668-675. DOI: 10.5220/0011691700003417

in Bibtex Style

@conference{visapp23,
author={Souha Mansour and Saoussen Ben Jabra and Ezzedine Zagrouba},
title={A Robust Deep Learning-Based Video Watermarking Using Mosaic Generation},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={668-675},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011691700003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP
TI - A Robust Deep Learning-Based Video Watermarking Using Mosaic Generation
SN - 978-989-758-634-7
AU - Mansour S.
AU - Ben Jabra S.
AU - Zagrouba E.
PY - 2023
SP - 668
EP - 675
DO - 10.5220/0011691700003417
PB - SciTePress