LSSD: a Controlled Large JPEG Image Database for Deep-Learning-based Steganalysis "into the Wild"
Abstract
For many years, the image databases used in steganalysis have been relatively small, i.e. about ten thousand images. This limits the diversity of images and thus prevents large-scale analysis of steganalysis algorithms. In this paper, we describe a large JPEG database composed of 2 million colour and grey-scale images. This database, named LSSD for Large Scale Steganalysis Database, was obtained thanks to the intensive use of \enquote{controlled} development procedures. LSSD has been made publicly available, and we aspire it could be used by the steganalysis community for large-scale experiments. We introduce the pipeline used for building various image database versions. We detail the general methodology that can be used to redevelop the entire database and increase even more the diversity. We also discuss computational cost and storage cost in order to develop images.
Keywords
Cite
@article{arxiv.2101.01495,
title = {LSSD: a Controlled Large JPEG Image Database for Deep-Learning-based Steganalysis "into the Wild"},
author = {Hugo Ruiz and Mehdi Yedroudj and Marc Chaumont and Frédéric Comby and Gérard Subsol},
journal= {arXiv preprint arXiv:2101.01495},
year = {2021}
}
Comments
ICPR'2021, International Conference on Pattern Recognition, MMForWILD'2021, Workshop on MultiMedia FORensics in the WILD, Lecture Notes in Computer Science, LNCS, Springer. January 10-15, 2021, Virtual Conference due to Covid (formerly Milan, Italy). Version of December 2020