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基于(7, 4)汉明码和单位平滑检测的加密图像可逆数据隐藏

Reversible Data Hiding in Encrypted Image Based on (7, 4) Hamming Code and UnitSmooth Detection.

作者信息

Li Lin, Chang Chin-Chen, Lin Chia-Chen

机构信息

Computer Engineering College, Ji Mei University, Xiamen 361021, China.

Department of Information Engineering and Computer Science, Feng Chia University, 100 Wenhwa Road, Seatwen, Taichung 40724, Taiwan.

出版信息

Entropy (Basel). 2021 Jun 22;23(7):790. doi: 10.3390/e23070790.

Abstract

With the development of cloud storage and privacy protection, reversible data hiding in encrypted images (RDHEI) plays the dual role of privacy protection and secret information transmission. RDHEI has a good application prospect and practical value. The current RDHEI algorithms still have room for improvement in terms of hiding capacity, security and separability. Based on (7, 4) Hamming Code and our proposed prediction/ detection functions, this paper proposes a Hamming Code and UnitSmooth detection based RDHEI scheme, called HUD-RDHEI scheme for short. To prove our performance, two database sets-BOWS-2 and BOSSBase-have been used in the experiments, and peak signal to noise ratio (PSNR) and pure embedding rate (ER) are served as criteria to evaluate the performance on image quality and hiding capacity. Experimental results confirm that the average pure ER with our proposed scheme is up to 2.556 bpp and 2.530 bpp under BOSSBase and BOWS-2, respectively. At the same time, security and separability is guaranteed. Moreover, there are no incorrect extracted bits during data extraction phase and the visual quality of directly decrypted image is exactly the same as the cover image.

摘要

随着云存储和隐私保护技术的发展,加密图像中的可逆数据隐藏(RDHEI)发挥着隐私保护和秘密信息传输的双重作用。RDHEI具有良好的应用前景和实用价值。当前的RDHEI算法在隐藏容量、安全性和可分离性方面仍有改进空间。基于(7, 4)汉明码和我们提出的预测/检测函数,本文提出了一种基于汉明码和单元平滑检测的RDHEI方案,简称为HUD-RDHEI方案。为了证明我们的性能,实验中使用了两个数据库集——BOWS-2和BOSSBase,并将峰值信噪比(PSNR)和纯嵌入率(ER)作为评估图像质量和隐藏容量性能的标准。实验结果证实,在BOSSBase和BOWS-2下,我们提出的方案的平均纯嵌入率分别高达2.556 bpp和2.530 bpp。同时,保证了安全性和可分离性。此外,在数据提取阶段没有错误提取的比特,直接解密图像的视觉质量与掩护图像完全相同。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff1f/8306628/1cf7e689cba7/entropy-23-00790-g001.jpg

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