基于动态差分扩展的强鲁棒数据库水印算法研究

doi:10.11959/j.issn.2096-109x.2023065

Abstract

Abstract:

A surge in the amount of information comes with the rapid development of the technology industry.Across all industries, there is a need to collect and utilize vast amounts of data.While this big data holds immense value, it also poses unprecedented challenges to the field of data security.As relational databases serve as a fundamental storage medium for data, they often contain large-scale data rich in content and privacy.In the event of a data leak, significant losses may occur, highlighting the pressing need to safeguard database ownership and verify data ownership.However, existing database watermarking technologies face an inherent tradeoff between improving watermark embedding capacity and reducing data distortion.To address this issue and enhance watermark robustness, a novel robust database watermarking algorithm based on dynamic difference expansion was introduced.The QR code was employed as the watermark, the SVD decomposition of the low frequency part of the image was utilized after Haar wavelet transform.By extracting specific feature values and using residual feature values as the watermark sequence, it was ensured that the same-length watermark sequence contains more information and the embedded watermark length can be reduced.Furthermore, by combining the adaptive differential evolution algorithm and the minimum difference algorithm, the optimal embedding attribute bits were selected to alleviate the problems of low computational efficiency, high data distortion and poor robustness of traditional difference expansion techniques in embedding watermarks, and to improve the embedding capacity of watermarks while reducing the distortion of data.Experimental results demonstrate that the proposed algorithm achieves a high watermark embedding rate with low data distortion.It is resilient against multiple attacks, exhibiting excellent robustness and strong traceability.Compared to existing algorithms, it offers distinct advantages and holds great potential for broad application in the field of data security.

Key words: database watermarking, differential evolution, difference expansion, singular value decomposition, Haar wavelet transform, quick response code

CLC Number:

TP393

Tianqi WANG, Yingzhou ZHANG, Yunlong DI, Dingwen LI, Linlin ZHU. Research on strong robustness watermarking algorithm based on dynamic difference expansion[J]. Chinese Journal of Network and Information Security, 2023, 9(5): 150-165.

Figures/Tables 19

小波变换	QR码像素为64×64		QR码像素为128×128		QR码像素为256×256
小波变换	压缩率	绝对最大差异	压缩率	绝对最大差异	压缩率	绝对最大差异
Haar	70.7 001 953	$5 . 4001247$	$78 . 3752441$	7.3 896 444	$93 . 3868408$	$9 . 094947$
Daubechies	68.8 720 703	1.0 516 032	74.2 797 851	1.2 221 335	82.588 195	1.4 687 962
Coiflet	$71 . 508789$	1.4 861 683	75.8 422 851	7.6 738 615	84.6 649 169	1.8 301 996
Symlet	69.2 626 953	1.0 461 462	74.1 210 937	$9 . 0750518$	84.1 064 453	2.1 753 364

小波变换	QR码像素为64×64		QR码像素为128×128		QR码像素为256×256
小波变换	压缩率	绝对最大差异	压缩率	绝对最大差异	压缩率	绝对最大差异
Haar	$69 . 5751953$	5.9 685 589	$83 . 203125$	7.3 896 444	$89 . 979553$	$7 . 6738615$
Daubechies	67.8 955 071	$9 . 3791641$	77.1 911 621	1.1 937 117	81.054 687	1.4 164 767
Coiflet	68.2 617 187	1.3 672 206	79.461 669	1.4 278 214	81.561 279	1.938 388
Symlet	68.2 617 187	5.6 616 581	77.746 582	$7 . 7305807$	81.381 225	2.1 638 108

属性	原始数据库		ADE-DDEW算法		GADEW算法		RF-GADEW算法		GAHSW算法		RF-GAHSW算法
属性	Mean	Std	Mean	Std	Mean	Std	Mean	Std	Mean	Std	Mean	Std
T9	19.483	2.015	19.476	2.009	19.082	2.382	19.575	2.133	$19 . 483$	$2 . 015$	19.453	2.035
RH_9	41.548	4.151	$41 . 541$	$4 . 143$	41.383	4.601	41.476	4.285	41.572	4.139	41.563	4.148
T_out	7.4	5.317	$7 . 389$	$5 . 308$	7.201	5.528	7.523	5.203	7.443	5.348	7.432	5.329
Press_mm_hg	79.769	7.399	79.757	7.405	79.829	7.021	79.892	7.256	79.737	7.371	$79 . 748$	$7 . 385$
RH_out	755.523	14.901	$755 . 518$	$14 . 901$	755.401	14.710	755.437	14.834	755.556	14.850	755.549	14.924
Windspeed	4.041	2.451	$4 . 052$	$2 . 459$	4.284	2.649	4.184	2.392	4.086	2.411	4.067	2.428
Visibility	38.342	11.794	$38 . 35$	$11 . 789$	38.742	11.620	38.492	11.630	38.393	11.756	38.361	11.775
Tdewpoint	3.753	4.195	$3 . 747$	$4 . 204$	3.932	4.348	3.629	4.275	3.702	4.264	3.737	4.218
rv1	24.986	14.496	$24 . 972$	$14 . 511$	24.847	14.730	24.816	14.583	24.932	14.558	24.971	14.526

属性	ADE-DDEW算法		GADEW算法		RF-GADEW算法		GAHSW算法		RF-GAHSW算法
属性	D_M	D_S	D_M	D_S	D_M	D_S	D_M	D_S	D_M	D_S
T9	0.001	0.002	0.023	0.063	0.002	0.192	$0$	$0$	0.003	0.045
RH_9	$0 . 001$	$0 . 003$	0.019	0.320	0.187	0.073	0.006	0.072	0.084	0.065
T_out	$0 . 002$	$0 . 001$	0.380	0.754	0.003	0.018	0.038	0.047	0.026	0.194
Press_mm_hg	0.002	0.006	0.049	0.128	0.218	0.007	0.014	0.005	$0$	$0$
RH_out	$0 . 003$	$0 . 002$	0.020	0.870	0.010	0.501	0.106	0.023	0.008	0.073
Windspeed	$0 . 001$	$0 . 001$	0.076	0.018	0.009	0.129	0.080	0.007	0.017	0.072
Visibility	$0 . 002$	$0 . 001$	0.890	0.342	0.227	0.284	0.004	0.039	0.080	0.160
Tdewpoint	$0 . 004$	$0 . 001$	0.017	0.050	0.001	0.547	0.072	0.216	0.021	0.015
rv1	$0 . 003$	$0 . 002$	0.430	0.081	0.039	0.028	0.193	0.074	0.047	0.007
MAE	$0 . 019$		2.914		1.315		0.852		0.516

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攻击比例	文本QR码	数字QR码	文本QR码	数字QR码	文本QR码	数字QR码
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