Abstract
Chaotic systems implemented by artificial neural networks are good candidates for data encryption. In this manner, this paper introduces the cryptographic application of the Hopfield and the Hindmarsh–Rose neurons. The contribution is focused on finding suitable coefficient values of the neurons to generate robust random binary sequences that can be used in image encryption. This task is performed by evaluating the bifurcation diagrams from which one chooses appropriate coefficient values of the mathematical models that produce high positive Lyapunov exponent and Kaplan–Yorke dimension values, which are computed using TISEAN. The randomness of both the Hopfield and the Hindmarsh–Rose neurons is evaluated from chaotic time series data by performing National Institute of Standard and Technology (NIST) tests. The implementation of both neurons is done using field-programmable gate arrays whose architectures are used to develop an encryption system for RGB images. The success of the encryption system is confirmed by performing correlation, histogram, variance, entropy, and Number of Pixel Change Rate (NPCR) tests.
Highlights
Image encryption is one of the well-known mechanisms to preserve confidentiality over a reliable unrestricted public channel
In this work we show the application of neural networks in the design of random number generators (RNGs), whose binary sequences are applied to implement an image encryption scheme [24]
This paper introduces the selection of the best coefficients of both Hopfield and Hindmarsh–Rose neurons, from the bifurcation diagram, to generate robust chaotic sequences that improve National Institute of Standard and Technology (NIST) tests and enhance chaotic encryption of images
Summary
Image encryption is one of the well-known mechanisms to preserve confidentiality over a reliable unrestricted public channel. The topic of image encryption remains open and several researchers are proposing the use of chaotic systems to mask information that can be transmitted in a secure channel In this direction, this paper highlights the usefulness of Hopfield and Hindmarsh–Rose neural networks to generate chaotic behavior, and its suitability to design random number generators (RNGs) that are implemented using field-programmable gate arrays (FPGAs). In this work we show the application of neural networks in the design of random number generators (RNGs), whose binary sequences are applied to implement an image encryption scheme [24]. This idea has been previously exploited, for example: the Hopfield neural network was used in [25] to design a RNG, but showed low randomness In this manner, this paper introduces the selection of the best coefficients of both Hopfield and Hindmarsh–Rose neurons, from the bifurcation diagram, to generate robust chaotic sequences that improve NIST tests and enhance chaotic encryption of images.
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