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

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 encrypti...

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Bibliographic Details
Published in:Neural computing & applications 2020-08, Vol.32 (15), p.11477-11489
Main Authors: Lakshmi, C., Thenmozhi, K., Rayappan, John Bosco Balaguru, Amirtharajan, Rengarajan
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Encryption
Image Processing and Computer Vision
Image transmission
Multimedia
Neural networks
Original Article
Parameter sensitivity
Probability and Statistics in Computer Science
Soft computing
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Summary: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.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-019-04637-4