基于图像多重隐写的区块链隐蔽通信方案

刘媛妮; 范飞; 赵宇洋; 张建辉; 周由胜

doi:10.11999/JEIT240798

基于图像多重隐写的区块链隐蔽通信方案

doi: 10.11999/JEIT240798

1.
重庆邮电大学网络空间安全与信息法学院重庆 400065
2.
国家数字交换系统工程技术研究中心郑州 450002

基金项目: 国家重点研发计划(2023YFF0905300)，国家重点研发计划(2023YFB3107405)，国家自然科学基金(62272076)

详细信息

作者简介:
刘媛妮：女，教授，博士，研究方向为物联网安全，车联网安全，身份认证等

范飞：男，硕士生，研究方向为数据隐蔽通信

赵宇洋：男，硕士生，研究方向为数据隐蔽通信

张建辉：男，研究员，博士，研究方向为路由和交换设计、路由协议、资源调度、网络安全和未来网络等

周由胜：男，教授，博士，研究方向为数据安全、认证与密钥协商等

通讯作者:
周由胜　zhouys@cqupt.edu.cn

中图分类号: TN918.91; TP309
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出版历程
- 收稿日期: 2024-09-14
- 修回日期: 2025-03-21
- 网络出版日期: 2025-04-02

A Convert Communication Scheme of Blockchain Based on Image Multilevel Steganography Embedding

1.
College of Cyberspace Security and Information Law, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
National Digital Switching System Engineering and Technological Research and Department Center, Zhengzhou 450002, China

Funds: The National Key Research and Development Program of China (2023YFF0905300), The National Key Research and Development Program of China (2023YFB3107405), The National Natural Science Foundation of China (62272076)

摘要

摘要: 针对现有基于图像隐写的区块链隐蔽通信方案利用传统深度学习方法面临的抗隐写分析能力低、信息嵌入率低及信息泄露等问题，本文提出一种基于图像多重隐写嵌入的隐蔽通信方案。首先，构造基于隐写器的多重对抗网络，通过生成对抗网络和隐写分析对抗网络的对抗迭代训练，生成更适合信息隐写的载密图像；其次，利用基于位置图信息的密文域可逆信息隐藏方法，将隐蔽信息嵌入至载密图像，生成含完整隐蔽信息的载密密文图像；最后，将载密密文图像存储至IPFS文件返回唯一标识，利用地址映射的方法将该标识存储至区块链网络中实现隐蔽传输。理论及实验结果表明，相较于传统基于深度学习的区块链隐蔽通信方案，本方案具备更强的抗隐写检测攻击能力和更高的信息嵌入容量，同时减少了通信时延。
- 隐蔽通信 /
- 区块链 /
- 对抗网络 /
- 信息隐藏
Abstract: Objective With the advancement of information technology, information security concerns have become increasingly significant, making covert communication technology a critical area of focus. Existing schemes face limitations regarding embedding rate, anti-detection, and communication efficiency. To address these issues, steganographic embedding methods based on Generative Adversarial Networks (GANs) have gained considerable attention. This study utilizes the iterative training of GAN and steganalysis adversarial networks to generate stego-images with enhanced anti-detection capabilities. This approach aims to meet the concealment requirements for secure information transmission, while also improving the communication efficiency and security of the information exchange. Methods This study proposes a blockchain-based covert communication scheme utilizing image multilevel steganography. First, a multiple adversarial network for steganography is constructed, generating stego-images with enhanced anti-detection capabilities through the adversarial iterative training of GAN and steganalysis adversarial networks. Next, a reversible data hiding method in the ciphertext domain, based on location map information, is employed to embed the hidden data into the stego-images, resulting in a stego-images that contains the complete hidden information. Finally, the ciphertext image is stored in the InterPlanetary File System (IPFS) to assign it a unique identity, and then mapped to an address in the blockchain to enable covert transmission. Results and Discussions To evaluate the effectiveness of the proposed scheme in terms of anti-steganography capability, invisibility, embedding capacity, and communication delay, simulation experiments are conducted. Regarding anti-steganography capability, the stego-images generated by the proposed scheme demonstrate strong anti-detection performance, outperforming the WOW and HILL algorithms (Fig. 7). In terms of concealment, the reversible data hiding method in the ciphertext domain, based on location map and spatial domain information, offers high concealment, effectively protecting the image content while enabling lossless restoration (Table 5, Table 6, Table 7). Concerning embedding capacity, the steganography algorithm in this scheme exhibits a high embedding capacity, with an average embedding rate exceeding that of the PBTL, IPBTL, and ERLC-BMPR algorithms (Fig. 9). Finally, in terms of communication delay, the proposed scheme results in low covert communication delay, outperforming the DVANET, BDLV, and L-TCM algorithms (Fig. 12). Conclusions This paper proposes a blockchain-based covert communication scheme utilizing image multilevel steganography. Simulation experiments validate its advantages in information embedding rate, anti-steganography detection capability, concealment, and communication delay. The results demonstrate the following: 1. In terms of anti-steganography ability, the anti-detection performance of stego-images generated by SRNet+Zhu-Net significantly exceeds that of the WOW and HILL methods; 2. Regarding invisibility and embedding capacity, the proposed reversible data hiding method in the encrypted domain, based on location map and spatial domain information, achieves a high embedding rate and lossless recovery, outperforming the PBTL, IPBTL, and ERLC-BMPR methods; 3. In terms of communication efficiency, this scheme significantly reduces communication delay by combining blockchain and IPFS. Future research will focus on homomorphic encryption and identity authentication mechanisms to further enhance the security of on-chain data.
- Covert communication /
- Blockchain /
- Adversarial network /
- Information hiding

HTML全文

图 1 方案总体流程图

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图 2 基于隐写器的多重对抗网络模型

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图 3 基于位置图信息的密文域可逆信息隐藏

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图 4 位置图信息组成

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图 5 隐蔽信息传输过程

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图 6 判别器损失变化

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图 7 不同对抗网络隐写器判别误差变化

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图 8 不同隐写算法在测试图像的最大嵌入率

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图 9 不同隐写算法在三种数据集的平均嵌入率

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图 10 不同通信方案的通信时延对比

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表 1 符号变量表

符号	描述
IS	信息发送方
IR	信息接收方
m₁	部分隐蔽信息
m₂	剩余隐蔽信息
m	隐蔽信息(m = m₁ + m₂)
X	原始图像
X_V	对抗图像样本
X_VM	对抗隐写图像
I	载体图像
I₁	载密图像
I_e	加密图像
I_LM	含位置图信息的加密图像
IR_pub	接收方公钥
IR_pri	接收方私钥
e	图像加密密钥(对称密钥)
S(I_m)	载密密文图像
$ \delta $	IPFS返回的唯一哈希标识

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表 2 像素标记值对应的编码序列

像素标记值	个数统计	概率分布	编码长度	编码序列
2	2	0.040 8	4	0010
3	11	0.224 5	2	01
5	3	0.061 2	3	000
6	31	0.632 7	1	1
8	2	0.040 8	4	0011

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表 3 隐写分析对抗网络消融实验结果

隐写分析对抗网络	平均PSNR	平均SSIM	对抗样本隐写检测(%)	隐写图像检测(%)	训练时间(min)
SRNet	45.325	0.979 2	52.3	88.9	962
Xu-Net	46.296	0.982 3	48.6	89.6	1 038
Zhu-Net+Xu-Net	42.539	0.956 3	51.2	87.8	1 369
SRNet+Zhu-Net+Xu-Net	39.689	0.862 4	49.6	86.4	1 738
Zhu-Net	44.569	0.991 4	51.4	89.2	965
SRNet+Zhu-Net	43.647	0.963 2	50.3	86.3	1 345

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表 4 判别器和隐写对抗网络损失权重对比

隐写对抗损失权重组合	平均PSNR	平均SSIM	SRNet(%)	Xu-Net(%)	Zhu-Net(%)	Ye-Net(%)
$ \alpha = 0.6, \beta = 0.4 $	38.637 9	0.954 8	50.6	50.1	50.1	49.7
$ \alpha = 0.3, \beta = 0.7 $	39.107 5	0.961 9	50.2	49.6	49.3	50.2
$ \alpha = 0.1, \beta = 0.9 $	39.123 6	0.963 0	49.3	50.3	50.2	49.6
$ \alpha = 0.2, \beta = 0.8 $	39.123 6	0.961 3	50.3	49.7	50.4	50.3
$ \alpha = 0.5, \beta = 0.5 $	38.864 7	0.954 7	49.3	50.6	50.1	50.8

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表 5 载密图像与加密图像的PSNR和SSIM值

载密图像/加密图像	PSNR	SSIM
Scenry	7.782 2	0.055 4
Building	8.670 3	0.060 0
Lena	10.072 9	0.021 9
Steamship	7.321 3	0.052 0
Cat	8.375 7	0.056 7

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表 6 载密图像与载密密文图像的PSNR和SSIM值

载密图像/载密密文图像	PSNR	SSIM
Scenry	7.813 3	0.059 2
Building	8.117 3	0.056 6
Lena	10.078 8	0.021 2
Steamship	7.256 4	0.053 1
Cat	8.330 4	0.058 3

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表 7 载密图像与还原后载密图像的PSNR和SSIM值

载密图像/还原后的载密图像	PSNR	SSIM
Scenry	$ + \infty $	1
Building	$ + \infty $	1
Lena	$ + \infty $	1
Steamship	$ + \infty $	1
Cat	$ + \infty $	1

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表 8 针对Scenry图像文献[24]的编码过程

标记值	标记值个数	概率分布	编码序列	嵌入容量	编码序列长度	净嵌入容量
–1	985	–	–	–	–	–
0	9 736	0.043	11010	1	5	–4
1	13 082	0.058	1100	2	4	–2
2	10 093	0.045	11011	3	5	–2
3	27 952	0.125	100	4	3	1
4	26 893	0.121	011	5	3	2
5	44 509	0.198	00	6	2	4
6	30 836	0.137	101	7	3	4
7	36 437	0.162	111	8	3	5
8	24 963	0.111	010	8	3	4
合计	224 501	1.000	–	1 286 558	706 697	579 861

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表 9 针对Scenry图像本文隐藏方法的编码过程

标记值	标记值个数	概率分布	编码序列	嵌入容量	编码序列长度	净嵌入容量
–1	985	–	–	–	–	–
0	9736	0.043	0100	1	4	–3
1	14 823	0.066	1110	2	4	–2
2	13 693	0.062	0101	3	4	–1
3	25 961	0.116	011	4	3	1
4	28 469	0.126	100	5	3	2
5	43 259	0.192	00	6	2	4
6	30 836	0.138	101	7	3	4
7	35 123	0.156	110	8	3	5
8	22 601	0.101	1111	8	4	4
合计	224 501	1	–	1 263 848	691 097	572 751(+131 072)

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