Institution profile

Ural Federal University

Academic institutioneurope · ru
Official website
Research library8linked papers
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Selected work

Representative Papers

Completely Reachable Road Coloring

Jul 13, 2026

This study investigates whether a directed graph admits an edge labeling over a finite alphabet that renders it a completely reachable automaton, and characterizes those graphs—termed fully labeling-robust—for which every possible labeling yields complete reachability. By integrating tools from graph theory, automata theory, and computational complexity, the work provides the first complete structural characterization of directed graphs that can realize completely reachable automata. The main contributions include a polynomial-time algorithm for recognizing such graphs, a proof that the decision problem is NP-complete when the alphabet size is fixed, and a full classification of fully labeling-robust graphs.

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

Jun 18, 2026

This work addresses the vulnerability of traditional image encryption schemes that rely on fixed S-boxes lacking input dependency, rendering them susceptible to linear and differential cryptanalysis. To overcome this limitation, the authors propose a novel hybrid approach integrating convolutional neural networks (CNNs) with classical cryptographic principles. Specifically, a pre-trained CNN extracts salient features from the input image to dynamically generate a personalized S-box for pixel substitution. This method represents the first implementation of content-adaptive S-box generation driven by the plaintext image itself, substantially enhancing the nonlinearity, uniqueness, and resilience of the encryption process against statistical and structural attacks. Experimental results demonstrate superior performance over conventional techniques across multiple security metrics, including information entropy, histogram uniformity, pixel correlation, NPCR, and UACI, thereby achieving both heightened security and greater flexibility.

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

Jan 28, 2026

This work proposes a novel image encryption framework that integrates fractal geometry with Fourier transform, addressing the longstanding challenge of simultaneously achieving high security, image fidelity, and computational efficiency in traditional methods. By introducing fractal structures into the frequency-domain encryption process for the first time, the proposed approach effectively overcomes the trade-off bottleneck between efficiency and reconstruction quality inherent in conventional schemes. Experimental results demonstrate that the method significantly accelerates encryption and decryption while preserving excellent image reconstruction fidelity, thereby offering both strong security and practical utility. These findings underscore its potential advantage for efficient and secure image transmission in real-world applications.

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Recent publications

Latest Papers

Completely Reachable Road Coloring

Jul 13, 2026

This study investigates whether a directed graph admits an edge labeling over a finite alphabet that renders it a completely reachable automaton, and characterizes those graphs—termed fully labeling-robust—for which every possible labeling yields complete reachability. By integrating tools from graph theory, automata theory, and computational complexity, the work provides the first complete structural characterization of directed graphs that can realize completely reachable automata. The main contributions include a polynomial-time algorithm for recognizing such graphs, a proof that the decision problem is NP-complete when the alphabet size is fixed, and a full classification of fully labeling-robust graphs.

0 citationsRead paper

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

Jun 18, 2026

This work addresses the vulnerability of traditional image encryption schemes that rely on fixed S-boxes lacking input dependency, rendering them susceptible to linear and differential cryptanalysis. To overcome this limitation, the authors propose a novel hybrid approach integrating convolutional neural networks (CNNs) with classical cryptographic principles. Specifically, a pre-trained CNN extracts salient features from the input image to dynamically generate a personalized S-box for pixel substitution. This method represents the first implementation of content-adaptive S-box generation driven by the plaintext image itself, substantially enhancing the nonlinearity, uniqueness, and resilience of the encryption process against statistical and structural attacks. Experimental results demonstrate superior performance over conventional techniques across multiple security metrics, including information entropy, histogram uniformity, pixel correlation, NPCR, and UACI, thereby achieving both heightened security and greater flexibility.

0 citationsRead paper

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

Jan 28, 2026

This work proposes a novel image encryption framework that integrates fractal geometry with Fourier transform, addressing the longstanding challenge of simultaneously achieving high security, image fidelity, and computational efficiency in traditional methods. By introducing fractal structures into the frequency-domain encryption process for the first time, the proposed approach effectively overcomes the trade-off bottleneck between efficiency and reconstruction quality inherent in conventional schemes. Experimental results demonstrate that the method significantly accelerates encryption and decryption while preserving excellent image reconstruction fidelity, thereby offering both strong security and practical utility. These findings underscore its potential advantage for efficient and secure image transmission in real-world applications.

0 citationsRead paper