The Memorable Image Encryption Algorithm Based on Neuron-Like Scheme

Abstract

A new image cryptosystem based on neuron-like scheme is proposed in this paper. As a nonlinear component of image cryptosystem, neuron-like learning algorithm can realize a kind of memorable diffusion algorithm, thus realizing the plaintext-related image encryption algorithm. Through the feedback operation, the inputs and weights of the neuron are regulated by the information of the image, and then they are used to diffuse the image information. According to the needs of application scenarios, the number of inputs of the neuron can be adjusted so that the image cryptosystem can be applied to different security requirements. The simulation results show that the proposed image encryption system is extremely sensitive to the secret keys, the equivalent keys and the plain images, and also possesses the high security and adaptive characteristics. The image cryptosystem can be applied to the actual applications of image information security.

Signal-Enhanced Cross-Coupled Map Lattice andApplication in Spatiotemporal Chaotic Hash Functions

“…A positive Lyapunov exponent typically signifies chaos, where small differences in initial conditions lead to exponentially diverging outcomes. This section compares the Lyapunov exponents of the traditional CCML and the signal-enhanced CCML (SE-CCML) to demonstrate the improvement in chaotic behavior 34 . For a given dynamical system defined by x n+1 = f (x n ), the Lyapunov exponent λ is calculated as:…”

Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap

“…Zhang et al [253] proposed an image encryption algorithm using a neuron-like scheme. The image information is used to adjust neuron weightings to achieve effective pixel diffusion.…”