arXiv subjects

Nikolai Safiullin

Publications and source records attributed to Nikolai Safiullin.

3 recordsLinked to original sources

arXiv2026

Image Encryption Algorithm Based on Convolutional Neural Networks and Dynamic S-Box Generation

The paper proposes a dynamic approach to image encryption, combining the use of Convolutional Neural Networks (CNNs) and classical cryptography to improve the security and flexibility of image encryption. The main concept is to create adaptive Substitution boxes (S-boxes) based on characteristics that are learned by a trained CNN. The CNN-based S-boxes can be relied on for more non-linearity, uniqueness, and input image dependence than the conventional fixed S-boxes because they are susceptible to the linear and differential attacks. This dynamic behaviour enhances the confusion property and makes it more resistant to statistical and structural attacks. The encryption algorithm consists of CNN-based feature extraction and the creation of a personalised S-box to replace the pixels. Entropy, histogram analysis, correlation, NPCR, and UACI enable security assessment of generated S-boxes based on the CNN, indicating that the scheme is more resilient and flexible than traditional ones.

cs.CR↗

arXiv2026

A High-Performance Fractal Encryption Framework and Modern Innovations for Secure Image Transmission

The current digital era, driven by growing threats to data security, requires a robust image encryption technique. Classical encryption algorithms suffer from a trade-off among security, image fidelity, and computational efficiency. This paper aims to enhance the performance and efficiency of image encryption. This is done by proposing Fractal encryption based on Fourier transforms as a new method of image encryption, leveraging state-of-the-art technology. The new approach considered here intends to enhance both security and efficiency in image encryption by comparing Fractal Encryption with basic methods. The suggested system also aims to optimise encryption/ decryption times and preserve image quality. This paper provides an introduction to Image Encryption using the fractal-based method, its mathematical formulation, and its comparative efficiency against publicly known traditional encryption methods. As a result, after filling the gaps identified in previous research, it has significantly improved both its encryption/decryption time and image fidelity compared to other techniques. In this paper, directions for future research and possible improvements are outlined for attention.

cs.CR↗

arXiv2024

Forecast of solar activity based on mean-field dynamo model and neural network

We discuss a prediction of the solar activity on a short time-scale applying the method based on a combination of a nonlinear mean-field dynamo model and the artificial neural network. The artificial neural network which serves as a correction scheme for the forecast, uses the currently available observational data (e.g., the 13 month running average of the observed solar sunspot numbers) and the dynamo model output. The nonlinear mean-field $α\,Ω$ dynamo produces the large-scale magnetic flux which is redistributed by negative effective magnetic pressure instability (NEMPI) producing sunspots and active regions. The nonlinear mean-field dynamo model includes algebraic nonlinearity (caused by the feedback of the growing magnetic field on the plasma motion) and dynamic nonlinearities (related to the dynamics of the magnetic helicity of small-scale magnetic field). We compare the forecast errors with a horizon of 1, 6, 12 and 18 months, for different forecast methods, with the same corrections on the current monthly observations. Our forecast is in good agreement with the observed solar activity, the forecast error is almost stably small over short-medium ranges of forecasting windows. Despite a strong level of chaotic component in the solar magnetic activity we present quantitative evidence that the solar activity on a short range can be stably well predicted, by the joint use of the physically based model with the neural network. This result may have an immediate practical implementation for predictions of various phenomena of solar activity and other astrophysical processes, so may be of interest to a broad community.

astro-ph.SR↗