A New Image Watermarking Technique based on Random Forests

DOI

10.2991/aeecs-18.2018.39How to use a DOI?

Keywords

image watermark; wavelet transform; random forests

Abstract

"Random Forests" is an algorithm developed by Breiman and Cutler in 2001[1]. It runs by constructing multiple decidion trees while training and outputting the classes that is the mode of the classes output by individual trees. It has improved performance over single decision trees, and it is much more efficient than traditional machine learning techniques, e.g. artificial neural networks and support vector machine. In this paper, a new image watermarking technique based on Random Forests RF is proposed. First, the image is decomposed through discrete wavelet transform. Then we use the relationship between the selected coefficient and its neighboring coefficients to train RF. Thanks to the good learning ability of RF, the watermark is adaptively embedded in wavelet domain and also can be extracted by the well trained RF. Experimental results show that the algorithm is robust against high intensity JPEG, JPEG2000, geometric distortion.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - San-ping Li
PY  - 2018/03
DA  - 2018/03
TI  - A New Image Watermarking Technique based on Random Forests
BT  - Proceedings of the 2018 2nd International Conference on Advances in Energy, Environment and Chemical Science (AEECS 2018)
PB  - Atlantis Press
SP  - 224
EP  - 227
SN  - 2352-5401
UR  - https://doi.org/10.2991/aeecs-18.2018.39
DO  - 10.2991/aeecs-18.2018.39
ID  - Li2018/03
ER  -