基于预测误差与位平面编码的密文域可逆信息隐藏算法

doi:10.11959/j.issn.2096-109x.2023081

摘要/Abstract

摘要：

随着云计算技术的发展，越来越多的用户使用云备份的方式存储一些重要文件，与此同时，人们对于隐私保护的要求日益增长。密文域可逆信息隐藏是一种可以在密文图像中嵌入秘密信息，保证无错提取秘密信息以及无损恢复原始图像的技术，该技术既保护了载体图像的安全性，又有效地在网络中传递隐私信息，能够应用于云环境实现对用户的隐私保护，受到研究者的广泛关注。针对现有密文域可逆信息隐藏算法嵌入率较低的问题，提出了基于预测误差与位平面编码的密文域可逆信息隐藏算法，该算法重点研究如何在保证安全性和可逆性的同时，实现高嵌入容量。所提算法对图像进行预处理，根据位平面分布的不同分别使用不同的方式编码，在图像中预留出大量空间，通过重排列将预留的空间放在低位平面中；使用密钥生成随机矩阵，对图像进行加密，以保证图像的安全性；将秘密信息嵌入预留的空间中。在接收端，合法接收者能够根据密钥可分离来实现秘密信息的无错提取以及原始图像的无损恢复。与现有算法相比，所提算法具有更高的嵌入率，在 BOWS-2、BOSSBase 和 UCID 这 3 个数据集上平均嵌入率分别达到了3.769 bit/pixel、3.874 bit/pixel 和 3.148 bit/pixel，分别比同类算法中性能最好的算法提升了 12.5%、6.9%和8.6%。实验结果表明，所提算法能够充分利用图像的冗余性，有效提升嵌入率。

关键词: 可逆信息隐藏, 密文域, 游程编码, 分离, 信息安全

Abstract:

With the increasing use of cloud backup methods for storing important files, the demand for privacy protection has also grown.Reversible data hiding in encrypted images (RDHEI) is an important technology in the field of information security that allows embedding secret information in encrypted images while ensuring error-free extraction of the secret information and lossless recovery of the original plaintext image.This technology not only enhances image security but also enables efficient transmission of sensitive information over networks.Its application in cloud environments for user privacy protection has attracted significant attention from researchers.A reversible data hiding method in encrypted images based on prediction error and bitplane coding was proposed to improve the embedding rate of existing RDHEI algorithms.Different encoding methods were employed by the algorithm depending on the distribution of the bitplanes, resulting in the creation of additional space in the image.The image was rearranged to allocate the freed-up space to the lower-order planes.Following this, a random matrix was generated using a key to encrypt the image, ensuring image security.Finally, the information was embedded into the reserved space.The information can be extracted and the image recovered by the receiver using different keys.The proposed algorithm achieves a higher embedding rate compared to five state-of-the-art RDHEI algorithms.The average embedding rates on BOWS-2, BOSSBase, and UCID datasets are 3.769 bit/pixel, 3.874 bit/pixel, and 3.148 bit/pixel respectively, which represent an improvement of 12.5%, 6.9% and 8.6% compared to the best-performing algorithms in the same category.Experimental results demonstrate that the proposed algorithm effectively utilizes the redundancy of images and significantly improves the embedding rate.

Key words: reversible data hiding, encrypted domain, run-length coding, separate, information security

中图分类号:

TP391

王海勇, 吉梦凝. 基于预测误差与位平面编码的密文域可逆信息隐藏算法[J]. 网络与信息安全学报, 2023, 9(6): 34-45.

Haiyong WANG, Mengning JI. Reversible data hiding algorithm in encrypted images based on prediction error and bitplane coding[J]. Chinese Journal of Network and Information Security, 2023, 9(6): 34-45.

图/表 16

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表1

位平面分类Table1 The classification of bitplane"

条件		类型	标志	编码结构
$4 n_{hb} \leq (M / S) (N / S)$		I	0	0+原始位平面比特流
$4 n_{hb} > (M / S) (N / S)$	且
$\begin{array}{l} l + ⌈ lb (M / S) (N / S) ⌉ \\ \geq (M / S) (N / S) \end{array}$		II	10	10+LM+NB
$4 n_{hb} > (M / S) (N / S)$	且
$\begin{array}{l} l + ⌈ lb (M / S) (N / S) ⌉ \\ < (M / S) (N / S) \end{array}$		III	11	11+l+LM'+NB

表1

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表2

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表6

图10

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加密图像	PSNR	SSIM
Lena	9.2337	0.0273
Baboon	9.5295	0.0681
Jetplane	8.3216	0.0301
Tiffany	9.4796	0.0332
Airplane	8.9663	0.0142
Man	9.103	0.0346

数据集	PNSR	SSIM
BOWS-2	+∞	1
BOSSBase	+∞	1
UCID	+∞	1

测试图像	PSNR	SSIM
Lena	+∞	1
Baboon	+∞	1
Jetplane	+∞	1
Tiffany	+∞	1
Airplane	+∞	1
Man	+∞	1

测试图像	能够压缩的位平面数量		HB数量		LM的总长度/bit		净嵌入容量/bit
测试图像	文献[14]算法	本文算法	文献[14]算法	本文算法	文献[14]算法	本文算法	文献[14]算法	本文算法
Lena	4	5	191 894	250 987	262 144	165 465	313 446	838 427
Baboon	3	3	100 459	137 063	196 608	134 035	104 697	414 079
Jetplane	6	6	249 925	283 759	393 216	207 185	356 427	927 780
Tiffany	4	4	182 534	214 437	262 144	133 508	285 366	724 080
Airplane	6	5	280 806	294 212	393 216	130 269	449 070	1 046 404
Man	4	5	181 523	238 940	262 144	198 585	282 333	757 097