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Image Encryption and Chaotic Cellular Neural Network

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Machine Learning in Cyber Trust

Machine learning has been playing an increasingly important role in information security and assurance. One of the areas of new applications is to design cryptographic systems by using chaotic neural network due to the fact that chaotic systems have several appealing features for information security applications. In this chapter, we describe a novel image encryption algorithm that is based on a chaotic cellular neural network. We start by giving an introduction to the concept of image encryption and its main technologies, and an overview of the chaotic cellular neural network. We then discuss the proposed image encryption algorithm in details, which is followed by a number of security analyses (key space analysis, sensitivity analysis, information entropy analysis and statistical analysis). The comparison with the most recently reported chaos-based image encryption algorithms indicates that the algorithm proposed in this chapter has a better security performance. Finally, we conclude the chapter with possible future work and application prospects of the chaotic cellular neural network in other information assurance and security areas.

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Authors and Affiliations

  1. College of Electronic Information Engineering, Chongqing University of Science and Technology, 400050, Chongqing, China

    Jun Peng

  2. Department of Computer Science, California State University, 95819, Sacramento, CA, USA

    Du Zhang

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  1. Jun Peng
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  2. Du Zhang
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Correspondence to Jun Peng .

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Peng, J., Zhang, D. (2009). Image Encryption and Chaotic Cellular Neural Network. In: Machine Learning in Cyber Trust. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-88735-7_8

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  • DOI: https://doi.org/10.1007/978-0-387-88735-7_8

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  • Print ISBN: 978-0-387-88734-0

  • Online ISBN: 978-0-387-88735-7

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