Abstract
In the current network and big data environment, the secure transmission of digital images is facing huge challenges. The use of some methodologies in artificial intelligence to enhance its security is extremely cutting-edge and also a development trend. To this end, this paper proposes a security-enhanced image communication scheme based on cellular neural network (CNN) under cryptanalysis. First, the complex characteristics of CNN are used to create pseudorandom sequences for image encryption. Then, a plain image is sequentially confused, permuted and diffused to get the cipher image by these CNN-based sequences. Based on cryptanalysis theory, a security-enhanced algorithm structure and relevant steps are detailed. Theoretical analysis and experimental results both demonstrate its safety performance. Moreover, the structure of image cipher can effectively resist various common attacks in cryptography. Therefore, the image communication scheme based on CNN proposed in this paper is a competitive security technology method.
Highlights
With the rapid development of cloud computing, big data, blockchain and other emerging technologies, the privacy and sharing of messages provides convenience for people in their work and daily lives [1,2,3,4]
As a significant transmission medium, digital images may include a lot of personal privacy, confidential information and other important data, so their privacy protection gets more attention [9,10,11,12]
The Message-Digest Algorithm 5 (MD5) can be used to disturb the initial value key parameters of cellular neural network (CNN) chaos; so that the key sequence changes with different plain images, the specific treatment methods are calculated using the following formulas:
Summary
Heping Wen 1,2,3 , Jiajun Xu 1 , Yunlong Liao 1 , Ruiting Chen 1 , Danze Shen 1 , Lifei Wen 1 , Yulin Shi 1 , Qin Lin 1 , Zhonghao Liang 1 , Sihang Zhang 1 , Yuxuan Liu 1 , Ailin Huo 1 , Tong Li 1 , Chang Cai 1 and Jiaqian Wen 1 and Chongfu Zhang 1,2, *. Guangdong Provincial Key Laboratory of Information Security Technology, Guangzhou 510006, China
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