@inproceedings{6343cbc7649b4bfb839cd495f2ca34a3,
title = "Reversible Image Watermarking Based on Deep Learning",
abstract = "Reversible image watermarking refers to technology that can restore an image to its original state after extracting the watermark. The scheme based on prediction error expansion (PEE) can achieve greater embedding capacity and less image distortion than other methods, so it has been widely researched in recent years. Prediction results of the predictor used by PEE are still not accurate enough, which limits the development of PEE. In this paper, a reversible watermarking predictor based on a deep neural network is proposed. Compared with other predictors, the prediction error histogram generated by our proposed predictor distributes more sharply. At the same time, because of the better prediction results, the watermarked image is closer to the original image. Experimental results show that the proposed method is effective and superior to the existing methods.",
keywords = "Image watermark, Prediction error expansion, Predictor, Reversible watermark",
author = "Jianchuan He and Linlin Tang and Jiawei Chen and Tao Qian and Shuhan Qi and Yang Liu and Jiajia Zhang",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 7th Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2021 ; Conference date: 29-05-2021 Through 31-05-2021",
year = "2022",
doi = "10.1007/978-981-16-8048-9\_26",
language = "英语",
isbn = "9789811680472",
series = "Smart Innovation, Systems and Technologies",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "275--284",
editor = "Jie-Fang Zhang and Chien-Ming Chen and Shu-Chuan Chu and Roumen Kountchev",
booktitle = "Advances in Intelligent Systems and Computing - Proceedings of the 7th Euro-China Conference on Intelligent Data Analysis and Applications",
address = "德国",
}