基于耦合神经P系统与区块链的遥感影像零水印版权保护方法

doi:10.11947/j.AGCS.2025.20250156

测绘学报 ›› 2025, Vol. 54 ›› Issue (12): 2247-2261.doi: 10.11947/j.AGCS.2025.20250156

基于耦合神经P系统与区块链的遥感影像零水印版权保护方法

侯昭阳¹^,²^,³(), 闫浩文¹^,²^,³(), 张黎明¹^,²^,³, 马荣娟¹^,²^,³, 屈睿涛¹^,²^,³

^1.兰州交通大学测绘与地理信息学院，甘肃　兰州　730070
^2.地理国情监测技术应用国家地方联合工程研究中心，甘肃　兰州　730070
^3.甘肃省测绘科学与技术重点实验室，甘肃　兰州　730070

收稿日期:2025-04-28 修回日期:2025-11-16 出版日期:2026-01-15 发布日期:2026-01-15
通讯作者: 闫浩文 E-mail:13230085@stu.lzjtu.edu.cn;yanhw@lzjtu.edu.cn
作者简介:侯昭阳（1996—），男，博士生，研究方向为空间数据安全。　E-mail：13230085@stu.lzjtu.edu.cn
基金资助:
国家自然科学基金(42271430; 42371463);甘肃省委组织部重点人才项目(2025RCXM012)

Zero-watermark copyright protection method for remote sensing images based on coupled neural P system and blockchain

Zhaoyang HOU¹^,²^,³(), Haowen YAN¹^,²^,³(), Liming ZHANG¹^,²^,³, Rongjuan MA¹^,²^,³, Ruitao QU¹^,²^,³

^1.Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
^2.National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China
^3.Gansu Provincial Key Laboratory of Science and Technology in Surveying & Mapping, Lanzhou 730070, China

Received:2025-04-28 Revised:2025-11-16 Online:2026-01-15 Published:2026-01-15
Contact: Haowen YAN E-mail:13230085@stu.lzjtu.edu.cn;yanhw@lzjtu.edu.cn
About author:HOU Zhaoyang (1996—), male, PhD candidate, majors in spatial data security. E-mail: 13230085@stu.lzjtu.edu.cn
Supported by:
The National Natural Science Foundation of China(42271430; 42371463);Key Talent Project of Gansu Provincial Organization Department(2025RCXM012)

摘要/Abstract

摘要：

当前主流零水印方法对遥感影像特有的光谱-空间多维特征挖掘存在不足，面对针对性攻击时稳健性较差，且依赖第三方知识产权管理机构，存在数据被篡改风险和交易互信度低的问题。为此，本文提出一种基于耦合神经P系统与区块链的遥感影像零水印版权保护方法。首先，采用非下采样剪切波变换对遥感影像的R、G、B波段分别进行多尺度分解得到相应的低频分量。其次，构建多层感知的耦合神经P系统模型，模拟神经元的耦合交互关系，挖掘各低频分量的时空动态特征；同时，引入多尺度形态梯度对模型的外部输入进行优化，以增强特征的空间相关性。然后，利用非对称Tent映射生成加密特征图像，并通过置乱与异或操作构建零水印。最后，结合Hyperledger Fabric和星际文件系统构建去中心化版权注册框架，通过智能合约实现版权信息的链上存证与自动验证。试验结果表明，在面对不同强度的几何攻击、非几何攻击及其组合攻击时，本文方法的归一化相关系数均稳定保持在0.99以上，展现出了较高的稳健性与抗攻击能力。

关键词: 遥感影像, 区块链, 耦合神经P系统, 零水印, 版权保护

Abstract:

Currently, mainstream zero-watermark approaches fail to adequately capture the unique spectral-spatial multidimensional characteristics of remote sensing images. This limitation renders them vulnerable to targeted attacks. Additionally, their reliance on third-party intellectual property management organizations introduces potential risks of data tampering and undermines mutual trust in transactions. To this end, a zero-watermark copyright protection method for remote sensing images based on coupled neural P system and blockchain is proposed. Firstly, the non-subsampled shearlet transform is used to obtain the corresponding low-frequency components by multi-scale decomposition of the R, G, and B bands of the remote sensing image, respectively. Secondly, a coupled neural P system model of multilayer perception is constructed to simulate the coupled interaction of neurons to extract the spatiotemporal dynamic features of each low-frequency component, and the external inputs of the model are optimized according to the multi-scale morphological gradient to enhance the spatial correlation of the features. Thirdly, an encrypted feature image is generated using an asymmetric Tent map, which is then scrambled and subjected to XOR operations to form the zero-watermark. Finally, a decentralized copyright registration framework is established by integrating Hyperledger Fabric and the Inter Planetary File System. This framework leverages smart contracts to facilitate the on-chain storage and automated verification of copyright information. The experimental results show that the normalized correlation coefficient of the proposed method stably stays above 0.99 in the face of different degrees of geometric, non-geometric, and combinatorial attacks, demonstrating high robustness and attack resistance.

Key words: remote sensing images, blockchain, coupled neural P system, zero-watermark, copyright protection

中图分类号:

P237

侯昭阳, 闫浩文, 张黎明, 马荣娟, 屈睿涛. 基于耦合神经P系统与区块链的遥感影像零水印版权保护方法[J]. 测绘学报, 2025, 54(12): 2247-2261.

Zhaoyang HOU, Haowen YAN, Liming ZHANG, Rongjuan MA, Ruitao QU. Zero-watermark copyright protection method for remote sensing images based on coupled neural P system and blockchain[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(12): 2247-2261.

图/表 13

图1

图2

图3

图4

图5

图6

表1

表2

图7

表3

非几何攻击的试验结果"

攻击方式	参数	PSNR	DFT-DCT^[11]		DCT-DFT^[12]		BEMD-DFT^[14]		NSST-SVD^[15]		本文方法
攻击方式	参数	PSNR	BER	NC	BER	NC	BER	NC	BER	NC	BER	NC
均值滤波	[3，3]	28.908	0.006	0.988	0.006	0.989	0.002	0.995	0.006	0.987	0.000	1.000
	[5，5]	26.672	0.010	0.979	0.009	0.981	0.004	0.992	0.009	0.981	0.000	1.000
	[7，7]	25.411	0.015	0.970	0.013	0.974	0.006	0.987	0.013	0.975	0.000	1.000
	[9，9]	24.552	0.020	0.960	0.017	0.966	0.009	0.982	0.016	0.968	0.000	0.999
中值滤波	[3，3]	29.102	0.018	0.964	0.017	0.966	0.003	0.993	0.004	0.991	0.000	1.000
	[5，5]	27.176	0.031	0.937	0.030	0.939	0.007	0.986	0.009	0.982	0.000	1.000
	[7，7]	25.997	0.044	0.912	0.042	0.915	0.010	0.979	0.013	0.974	0.000	0.999
	[9，9]	25.168	0.055	0.889	0.053	0.893	0.014	0.972	0.016	0.967	0.001	0.998
高斯滤波	[3，3]	29.858	0.005	0.989	0.005	0.990	0.003	0.994	0.006	0.989	0.000	1.000
	[5，5]	28.586	0.007	0.985	0.007	0.987	0.004	0.991	0.007	0.985	0.000	1.000
	[7，7]	28.367	0.008	0.984	0.007	0.986	0.006	0.987	0.008	0.984	0.000	0.999
	[9，9]	28，247	0.008	0.984	0.007	0.986	0.007	0.986	0.008	0.984	0.001	0.998
维纳滤波	[3，3]	43.026	0.002	0.997	0.001	0.997	0.000	1.000	0.000	0.999	0.000	1.000
	[5，5]	38.980	0.002	0.995	0.002	0.996	0.000	0.999	0.001	0.999	0.000	1.000
	[7，7]	36.787	0.003	0.994	0.003	0.994	0.000	0.999	0.001	0.998	0.000	1.000
	[9，9]	35.386	0.004	0.992	0.004	0.992	0.001	0.998	0.001	0.998	0.000	1.000
压缩	10	27.848	0.037	0.925	0.037	0.926	0.013	0.972	0.005	0.989	0.006	0.988
	30	32.496	0.013	0.974	0.013	0.975	0.005	0.991	0.001	0.997	0.001	0.999
	50	34.567	0.008	0.983	0.008	0.985	0.003	0.996	0.001	0.998	0.000	1.000
锐化	5	23.978	0.031	0.937	0.030	0.940	0.006	0.987	0.010	0.980	0.000	0.999
	7	21.390	0.043	0.914	0.042	0.916	0.011	0.978	0.015	0.970	0.001	0.998
	9	19.618	0.054	0.892	0.052	0.894	0.015	0.970	0.020	0.960	0.002	0.996
椒盐噪声	0.03	20.567	0.040	0.918	0.039	0.921	0.006	0.988	0.009	0.982	0.001	0.998
	0.05	18.345	0.046	0.907	0.044	0.910	0.008	0.983	0.012	0.976	0.001	0.997
	0.1	15.333	0.063	0.872	0.062	0.873	0.012	0.976	0.018	0.963	0.001	0.997
高斯噪声	0.03	19.757	0.032	0.936	0.032	0.934	0.009	0.982	0.011	0.977	0.000	0.999
	0.05	19.169	0.039	0.921	0.043	0.923	0.013	0.974	0.015	0.969	0.001	0.998
	0.1	17.156	0.048	0.903	0.046	0.906	0.018	0.964	0.021	0.958	0.001	0.998
泊松噪声	—	28.047	0.012	0.975	0.012	0.976	0.002	0.996	0.002	0.996	0.000	1.000

表3

图8

表4

图9

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影像	飞机场1	飞机场2	储油罐1	储油罐2	立交桥1	立交桥2	操场1	操场2
飞机场1	1.000 0	0.658 3	0.528 9	0.482 1	0.379 4	0.472 9	0.527 3	0.594 6
飞机场2	0.658 3	1.000 0	0.563 2	0.529 4	0.527 8	0.514 7	0.612 9	0.631 8
储油罐1	0.528 9	0.563 2	1.000 0	0.624 8	0.492 4	0.472 8	0.492 7	0.548 7
储油罐2	0.482 1	0.529 4	0.624 8	1.000 0	0.484 5	0.527 3	0.523 8	0.524 3
立交桥1	0.379 4	0.527 8	0.492 4	0.484 5	1.000 0	0.652 8	0.638 1	0.591 3
立交桥2	0.472 9	0.514 7	0.472 8	0.527 3	0.652 8	1.000 0	0.617 7	0.583 6
操场1	0.527 3	0.612 9	0.492 7	0.523 8	0.638 1	0.617 7	1.000 0	0.683 5
操场2	0.594 6	0.631 8	0.548 7	0.524 3	0.591 3	0.583 6	0.683 5	1.000 0

攻击方式	参数	PSNR	DFT-DCT^[11]		DCT-DFT^[12]		BEMD-DFT^[14]		NSST-SVD^[15]		本文方法
攻击方式	参数	PSNR	BER	NC	BER	NC	BER	NC	BER	NC	BER	NC
裁剪	左上角1/4	13.339	0.184	0.629	0.184	0.628	0.077	0.843	0.085	0.827	0.003	0.994
裁剪	中心1/4	12.950	0.201	0.593	0.197	0.605	0.085	0.827	0.102	0.794	0.003	0.994
翻转	上下	13.750	0.491	0.102	0.497	0.067	0.014	0.972	0.018	0.963	0.000	1.000
翻转	左右	13.612	0.503	0.062	0.489	0.128	0.020	0.959	0.021	0.957	0.000	1.000
旋转	5°	12.962	0.255	0.482	0.065	0.867	0.038	0.922	0.083	0.832	0.001	0.997
	10°	11.403	0.262	0.469	0.097	0.822	0.056	0.886	0.092	0.814	0.001	0.996
	20°	10.144	0.274	0.447	0.109	0.776	0.103	0.791	0.115	0.767	0.002	0.995
缩放	0.25	26.206	0.028	0.944	0.027	0.945	0.002	0.995	0.003	0.995	0.000	1.000
	0.5	28.128	0.025	0.948	0.025	0.950	0.002	0.996	0.002	0.995	0.000	1.000
	2	38.847	0.002	0.997	0.001	0.997	0.000	0.999	0.000	1.000	0.000	1.000
	4	39，187	0.002	0.997	0.001	0.998	0.000	1.000	0.000	1.000	0.000	1.000

基于耦合神经P系统与区块链的遥感影像零水印版权保护方法

Zero-watermark copyright protection method for remote sensing images based on coupled neural P system and blockchain

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