Моделирование и разработка системы внедрения и распознавания скрытых изображений
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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Modeling and development of a system for implementing and recognizing latent images

idMaslova O.I. Shagrova G.V.  

UDC 004.94
DOI: 10.26102/2310-6018/2020.29.2.022

  • Abstract
  • List of references
  • About authors

If earlier the problem of authenticity concerned only printing products (securities, documents, tickets, money, etc.), today the protection of documents presented in digital form (scanned documents, photographs and other multimedia documents) is no less relevant. Therefore, the creation of systems for embedding hidden information in secured documents by various methods is an urgent task, because It will allow you to protect documents, as well as in disputed situations, confirm their authenticity or copyright. In this work, a mathematical model of the system for embedding and recognizing hidden images is developed, which differs from its analogues in the possibility of expanding the many used functions for embedding and recognition. In contrast to the well-known unified model of a system for embedding information in digital signals, a model is proposed that allows implementing the modular principle of constructing a system for recognizing images containing hidden information introduced by various methods. Also, on the basis of the constructed system model, an information system for the implementation and recognition of hidden images, consisting of three interconnected software modules, was developed. This system is a graphical shell for models developed in MATLAB. This approach allows you to integrate the system, add new ones or make changes to existing modules, make changes to the developed model.

1. Fedoseev V.A., Mitekin V.A. Method for extracting watermarks from textured printing documents. Computer optics. 2014; 38 (4): 825-832.

2. Shevelev, A.A. Latent imaging using stochastic raster structures. Technology and technology of education. 2009; 1-2 (23-24): 226-233.

3. Topchiev, I.N.Program module for image filtering visualized magnetic signalograms. Strategic planning innovation and ways of commercializing scientific and technical products. Astrakhan University Publishing House. 2008; 1: 5-8.

4. Zharkikh A.A., Shagrova G.V., Drozdova V.I. Mathematical modeling and numerical control method for latent images. Bulletin of SevKavGTI. 2017; 30 (3): 118-121.

5. Nyeem, H. Developing a digital image watermarking model. International Conference on Digital Image Computing Techniques and Applications. 2011;1(19):468-473.

6. Nyeem, H. Digital image watermarking: its formal model, fundamental properties and possible attacks. EURASIP Journal on Advances in Signal Processing. 2014;1(1):1-22.

7. Zharkikh, A.A. Latent Image Recognition System for robotic complex. Actual problems of modern science: IV International Scientific and Practical Conference, SevKavGTI Publishing House, 2015; 2: 164- 167.

8. Fedoseev V.A. A unified model of systems for embedding information in digital signals. Computer optics ,. 2016; 40 (1): 87-98.

9. Maslova O.I, Shagrova G.V. An algorithm for implementation and recognition of hidden images based on discrete waves of transformation and singular decomposition of matrices. Student science for the development of the information society. X All-Russian scientific and technical Conference with international participation. Publishing house of NCFU. 2019; 2: 444- 452.

10. Zharkikh A.A., Shagrova G.V., Maslova O.I. Batch wavelet decomposition and analysis latent image obtained by variation of the direction of the lines. Modern science and innovation. 2018; 24 (4): 11-19.

11. Cohen, A.S. The gaussian watermarking game. IEEE Transactions on Information Theory. 2002;6(48):1639-1667.

Maslova Oksana Igorevna

Email: oksmaslova@inbox.ru


North Caucasian Federal University (NCFU)

Stavropol, Russian Federation

Shagrova Galina Vyacheslavovna
Doctor Of Physico-Mathematical Sciences
Email: g_shagrova@mail.ru

North Caucasian Federal University (NCFU)
Institute Of Information Technologies And Telecommunications

Stavropol, Russian Federation

Keywords: latent image, wavelet analysis, recognition of hidden images, decomposition by singular numbers, image embedding system

For citation: Maslova O.I. Shagrova G.V. Modeling and development of a system for implementing and recognizing latent images. Modeling, Optimization and Information Technology. 2020;8(2). Available from: https://moit.vivt.ru/wp-content/uploads/2020/05/MaslovaShagrova_2_20_1.pdf DOI: 10.26102/2310-6018/2020.29.2.022 (In Russ).


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