4.7 Article

Reversible Data Hiding in Encrypted 2D Vector Graphics Based on Reversible Mapping Model for Real Numbers

期刊

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIFS.2019.2899520

关键词

Data security; reversible data hiding in encrypted domain; reversible mapping model; 2D vector graphics; cloud storage

资金

  1. National Natural Science Foundation of China [61572182, 61370225]
  2. Hunan Provincial Natural Science Foundation of China [15JJ2007]
  3. State Scholarship Fund through the China Scholarship Council [201806130131]

向作者/读者索取更多资源

Currently, much attention has been paid to reversible data hiding (RDH) in an encrypted domain due to the popular deployment of cloud storage. However, nearly all existing RDH schemes in the encrypted domain are proposed for raster images, and very little work has been done to 2D vector graphics, which are represented in real numbers. In this paper, a reversible mapping model for real numbers is first built. It maps the points in R-n to 2(s) non-intersecting subsets in R-n, which guarantees that s bits can be embedded into each real number. Rased on the model, an RDH scheme in encrypted 2D vector graphics is put forward. In the scheme, a user encrypts 2D engineering graphics and stores them in the cloud, and then the cloud service provider can perform information hiding, extraction, and even recover the encrypted 2D vector graphics. For the authorized user, it can acquire the recovered 2D vector graphics from the cloud and obtain their original versions after decryption. For an unauthorized user, he can only acquire the encrypted 2D vector graphics with a hidden message, and only approximate 2D vector graphics can be obtained even if he knows the decryption key but does not know the hiding key. The experimental results and analysis show that it can strike a good balance between security, distortion, and capacity. It provides a new paradigm for RDH in the encrypted domain for the data represented in real numbers.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据