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Abstract

To improve the security of image encryption in singular chaotic systems, an encryption algorithm based on deep-learning is proposed in this paper. To begin with, the chaos sequence is generated by using a hyperchaotic Lorenz system, prior to creating new chaotic signals based on chaotic characteristics obtained from he simulations of the powerful complex network structure of long-short term memory artificial neural network (LSTM-ANN). Then, dynamic characteristics of the new signals are analyzed with the largest Lyapunov exponent, 0-1 test, power spectral analysis, phase diagrams and NIST test. In the end, the new signals are applied to image encryption, the results of which verify the expected increased difficulty in attacking the encrypted system. This is attributable to the differences of the new signals generated using the proposed method from the original chaotic signals, as well as arises from the high complexity and nonlinearity of the system. Considering its ability to withstand common encryption attacks, it is hence reasonable to conclude that the proposed method exhibits higher safety and security than other traditional methods.

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Authors and contacts

Corresponding author:Zhou Shuang,zhoushuang@cqnu.edu.cn; Wang Xing-Yuan,xywang@dlmu.edu.cn

Funds:Project supported by the National Natural Science Foundation of China (Grant No. 61672124), the Password Theory Project of the 13th Five-Year Plan National Cryptography Development Fund, China (Grant No. MMJJ20170203), the Liaoning Province Science and Technology Innovation Leading Talents Program Project, China (Grant No. XLYC1802013), the Key R&D Projects of Liaoning Province, China (Grant No. 2019020105-JH2/103), the Jinan City’ 20 Universities’ Funding Projects-Introducing Innovation Team Program, China (Grant No. 2019GXRC031), and the Science and Technology Research Program of Chongqing Municipal Education Commission, China (Grant No. KJQN201900529)

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References

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Cited By

DownLoad: Full-Size Img PowerPoint

统计测试	p值	结果
单比特频率测试	0.8752	通过
块内频率测试	0.8523	通过
游程测试	0.6121	通过
块内最长1游程测试	0.0828	通过
二进制矩阵秩测试	0.1445	通过
离散傅里叶(谱)测试	0.8152	通过
非重叠模板匹配测试	0.3527	通过
重叠模板匹配测试	0.4305	通过
Maurer通用统计测试	0.4214	通过
线性复杂度测试	0.2341	通过
序列测试	0.3053	通过
近似熵测试	0.1568	通过
累加和测试	0.3257	通过
随机旅行测试	0.1523	通过
随机旅行变种测试	0.1057	通过

DownLoad: CSV

统计测试	p值	结果
单比特频率测试	0.8815	通过
块内频率测试	0.7253	通过
游程测试	0.5986	通过
块内最长1游程测试	0.0823	通过
二进制矩阵秩测试	0.1263	通过
离散傅里叶(谱)测试	0.8164	通过
非重叠模板匹配测试	0.3580	通过
重叠模板匹配测试	0.5216	通过
Maurer通用统计测试	0.4418	通过
线性复杂度测试	0.5052	通过
序列测试	0.6015	通过
近似熵测试	0.1435	通过
累加和测试	0.4863	通过
随机旅行测试	0.3997	通过
随机旅行变种测试	0.2265	通过

DownLoad: CSV

信号来源	最大Lyapunov 指数	0-1 测试	功率谱分析	相图	NIST 测试
超混沌Lorenz	0.3381^[52]	0.7937	混沌	混沌	随机
深度学习	2.6002	0.9250	混沌	混沌	随机

DownLoad: CSV

图片	改变$ P(256, 256) $
图片	NPCR/%	UACI/%
Bird ($ 256 \times 256 $)	99.56	33.35
Cameraman ($ 256 \times 256 $)	99.63	33.30
Pepper ($ 256 \times 256 $)	99.62	33.39
House ($ 256 \times 256 $)	99.63	33.37
Lena ($ 512 \times 512 $)	99.58	33.38
Airplane ($ 512 \times 512 $)	99.59	33.42
Tank ($ 512 \times 512 $)	99.61	33.49
Splash ($ 512 \times 512 $)	99.64	33.54
Truck ($ 512 \times 512 $)	99.60	33.47
Airport ($ 1024 \times 1024 $)	99.62	33.48
Airplane ($ 1024 \times 1024 $)	99.60	33.47

DownLoad: CSV

	本文平均值	文献[10]	文献[13]	文献[58]	文献[59]
NPCR/%	99.604	99.61	99.6641	99.61	99.6114
UACI/%	33.46	33.48	33.6124	33.46	33.4523

DownLoad: CSV

图片	明文				密文
图片	水平	垂直	对角	反对角	水平	垂直	对角	反对角
Lena	0.9844	0.9668	0.9620	0.9790	–0.0016	0.0014	–0.0014	–0.0010
Bird	0.9889	0.9826	0.9713	0.9519	0.0114	0.0103	0.0104	–0.0031
Cameraman	0.9591	0.9335	0.9101	0.9377	–0.0099	0.0141	–0.0165	–0.0028
Pepper	0.9638	0.9585	0.9368	0.9339	–0.0127	–0.0014	–0.0079	0.0134
Airport	0.9066	0.9072	0.8446	0.8752	–0.0091	0.0088	–0.0014	–0.0054
Splash	0.9925	0.9850	0.9797	0.9507	0.0082	0.0016	0.0039	–0.0060
Airplane	0.9476	0.9664	0.9418	0.9299	–0.0278	–0.0105	0.0013	–0.0083
House	0.9549	0.9780	0.9399	0.9027	0.0141	–0.0115	–0.0176	0.0005
Tank	0.8678	0.8815	0.8414	0.7949	–0.0036	0.0003	–0.0098	–0.0065
Truck	0.9258	0.9561	0.9114	0.8194	0.0028	–0.0041	0.0018	0.0074

DownLoad: CSV

	Lena	文献[10]	文献[13]	文献[58]	文献[59]
水平	–0.0016	–0.00007	0.00047	–0.00251	–0.038118
垂直	0.0014	–0.0024	–0.03911	–0.00292	–0.029142
对角	–0.0014	0.0019	0.00305	–0.00156	0.002736

DownLoad: CSV

图片	图片大小	信息熵
House	$ 256 \times 256 $	7.9969
Cameraman	$ 256 \times 256 $	7.9971
Bird	$ 256 \times 256 $	7.9968
Pepper	$ 256 \times 256 $	7.9971
Lena	$ 512 \times 512 $	7.9993
Splash	$ 512 \times 512 $	7.9993
Airplane	$ 512 \times 512 $	7.9993
Tank	$ 512 \times 512 $	7.9994
Truck	$ 512 \times 512 $	7.9993
Airport	$ 1024 \times 1024 $	7.9998
Airplane	$ 1024 \times 1024 $	7.9998

DownLoad: CSV

	NPCR	UACI	信息熵	相关系数
	NPCR	UACI	信息熵	水平	垂直		对角
全黑图	0.9958	0.3332	7.9970	0.0016		0.0003	0.0036
全白图	0.9961	0.3351	7.9971	0.0070		0.0004	0.0070

DownLoad: CSV

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Vol. 70, Issue 23 - 2021-12-05

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