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Hopfield attractor-trusted neural network: an attack-resistant image encryption

  • C. Lakshmi
  • K. Thenmozhi
  • John Bosco Balaguru Rayappan
  • Rengarajan AmirtharajanEmail author
Original Article
  • 6 Downloads

Abstract

The recent advancement in multimedia technology has undoubtedly made the transmission of objects of information efficiently. Interestingly, images are the prominent and frequent representations communicated across the defence, social, private and aerospace networks. Image ciphering or image encryption is adopted as a secure medium of the confidential image. The utility of soft computing for encryption looks to offer an uncompromising impact in enhancing the metrics. Aligning with neural networks, a Hopfield attractor-based encryption scheme has proposed in this work. The parameter sensitivity, random similarity and learning ability have been instrumental in choosing this attractor for performing confusion and diffusion. The uniqueness of this scheme is the achievement of average entropy of 7.997, average correlation of 0.0047, average NPCR of 99.62 and UACI of 33.43 without using any other chaotic maps, thus proposing attack-resistant image encryption against attackable chaotic maps.

Keywords

Hopfield neural attractor Image-specific key generation Adaptive random sequence generator Permutation Substitution Image encryption 

Notes

Acknowledgements

The authors wish to acknowledge SASTRA Deemed University, Thanjavur, India, for extending infrastructural support to carry out this work.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Electrical and Electronics EngineeringSASTRA Deemed UniversityThanjavurIndia

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