Application of Mathematical Method in Image Processing and Information Hiding

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 776

Special Issue Editors


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Guest Editor
Instituto Politecnico Nacional, SEPI ESIME Culhuacan, Av. Sta. Ana 1000, San Francisco Culhuacan, Culhuacan CTM V, Coyoacan, Ciudad de México 04440, Mexico
Interests: image processing; robust; fragile and semi-fragile watermarking; reversible data hiding; steganography; information security; deep learning

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Guest Editor
Facultad de Ingenieria, Universidad Nacional Autonoma de Mexico, Avenida Universidad 3000 Ciudad Universitaria, Coyoacan, Mexico City 04510, Mexico
Interests: security of information systems; image watermarking; image segmentation and classification; theory and applications of error correcting codes; turbo codification; image filtering

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Guest Editor
Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Av. Eugenio Garza Sada 2501, Monterrey 64849, Mexico
Interests: image and video processing; robust video watermarking; steganography; computer vision; information security; innovation in education

Special Issue Information

Dear Colleagues,

Information hiding is a broad field that employs mathematical procedures for data hiding, watermarking, and steganography, which have been widely used in various application fields, including information security, digital forensics, and copyright management of digital images. Nowadays, with the rapid growth of digital media, there has been an increasing demand for efficient, adaptive, and effective techniques for data hiding, watermarking, and steganography in digital imaging. One of the newest issues in data hiding and steganography is the use of image processing, mathematical procedures, deep learning, and neural networks to develop more sophisticated concealment and detection methods. Another emerging issue is the application of data hiding in the context of cybersecurity and blockchain technology. In addition to these main topics, we also welcome papers in various fields that apply image processing, deep learning, and the neural network.

This Special Issue aims to publish significant contributions that consider theoretical approaches and practical solutions to image processing and information hiding. Topics of interest include, but are not limited to, the following:

  • Steganography and steganalysis;
  • Deep-learning and neural networks in data hiding, steganography, and watermarking;
  • Watermarking and digital rights management;
  • Blockchain-based image watermarking;
  • Security and cryptography in information hiding;
  • Visual illusions in data hiding applications;
  • Steganography and data hiding in social media;
  • Applications of image processing in various fields;
  • Information hiding in multimedia and biometrics;
  • Applications of data hiding, watermarking, and steganography in various fields.

I look forward to receiving your contributions.

Prof. Dr. Manuel Cedillo-Hernandez
Prof. Dr. Francisco Javier Garcia-Ugalde
Dr. Antonio Cedillo-Hernández
Guest Editors

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Keywords

  • image processing
  • information security
  • information hiding
  • steganography
  • visual cryptography
  • reversible data hiding
  • visible and invisible watermarking
  • steganography with visual illusion
  • fragile watermarking
  • deep-learning watermarking
  • semi-fragile watermarking
  • zero-watermarking
  • robust watermarking

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Published Papers (1 paper)

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Research

19 pages, 6807 KiB  
Article
Symmetric Grayscale Image Encryption Based on Quantum Operators with Dynamic Matrices
by Luis Olvera-Martinez, Manuel Cedillo-Hernandez, Carlos Adolfo Diaz-Rodriguez, Leonardo Faustinos-Morales, Antonio Cedillo-Hernandez and Francisco Javier Garcia-Ugalde
Mathematics 2025, 13(6), 982; https://doi.org/10.3390/math13060982 - 17 Mar 2025
Viewed by 323
Abstract
Image encryption is crucial for ensuring the confidentiality and integrity of digital images, preventing unauthorized access and alterations. However, existing encryption algorithms often involve complex mathematical operations or require specialized hardware, which limits their efficiency and practicality. To address these challenges, we propose [...] Read more.
Image encryption is crucial for ensuring the confidentiality and integrity of digital images, preventing unauthorized access and alterations. However, existing encryption algorithms often involve complex mathematical operations or require specialized hardware, which limits their efficiency and practicality. To address these challenges, we propose a novel image encryption scheme based on the emulation of fundamental quantum operators from a multi-braided quantum group in the sense of Durdevich. These operators—coproduct, product, and braiding—are derived from quantum differential geometry and enable the dynamic generation of encryption values, avoiding the need for computationally intensive processes. Unlike quantum encryption methods that rely on physical quantum hardware, our approach simulates quantum behavior through classical computation, enhancing accessibility and efficiency. The proposed method is applied to grayscale images with 8-, 10-, and 12-bit depth per pixel. To validate its effectiveness, we conducted extensive experiments, including visual quality metrics (PSNR, SSIM), randomness evaluation using NIST 800-22, entropy and correlation analysis, key sensitivity tests, and execution time measurements. Additionally, comparative tests against AES encryption demonstrate the advantages of our approach in terms of performance and security. The results show that the proposed method provides a high level of security while maintaining computational efficiency. Full article
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