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DOI: 10.14489/vkit.2021.06.pp.012-018
Варюхин В. А., Левина А. Б.
МЕТОД СТЕГАНОГРАФИЧЕСКОГО ВСТРАИВАНИЯ ИНФОРМАЦИИ НА ОСНОВЕ НЕПРЕРЫВНОГО ВЕЙВЛЕТ-ПРЕОБРАЗОВАНИЯ
(с. 12-18)
Аннотация. Рассмотрено непрерывное вейвлет-преобразование как метод стеганографического встраивания конфиденциальной информации в изображение. Главная цель стеганографии – скрыть информацию таким образом, чтобы возможность обнаружения данных была минимальна. Это делается путем сокрытия сообщения внутри контейнера на основе принципа незаметности. Методы стеганографии делятся на две группы: пространственные (визуально ухудшают качество изображения за счет непосредственного изменения компонент) и частотные (взаимодействуют с частотными характеристиками, что значительно лучше сказывается на качестве преобразованного изображения). Сейчас все чаще на смену распространенного частотного метода – дискретно-косинусного преобразования – приходит вейвлет-преобразование. Такой метод встраивания конфиденциальной информации визуально менее заметен для человеческого зрения. Представлены характеристики преобразования Хаара, а также изображения, демонстрирующие принцип работы вейвлет-преобразования. Разработан алгоритм стеганографии на основе вейвлет-преобразования, реализованный в среде Matlab. Представлен анализ полученных результатов на примере тестового изображения. Описаны достоинства данного метода.
Ключевые слова: вейвлет; стеганография; изображение; алгоритм; вейвлет Хаара; фильтр низких частот; фильтр высоких частот; коэффициенты аппроксимации.
Variukhin V. А., Levina A. В.
METHOD FOR STEGANOGRAPHIC HIDE OF INFORMATION BASED ON CONTINUOUS WAVELET TRANSFORM
(pp. 12-18)
Abstract. This article discusses continuous wavelet transform as a method of steganographic embedding of confidential information into an image. The main purpose of steganography is to hide information so that the possibility of data detection is minimized. This is done by hiding the message inside the container so that outsiders are not aware of the secret’s existence. Thus, the main principle of steganography is the principle of invisibility, which is also the basis of security when using these systems to transfer information. Steganography methods are divided into two large groups: spatial and frequency. The former visually degrade the image quality by directly changing the components (pixels). The latter interact with frequency characteristics, which has the best effect on the quality of the converted image. At this point in time, one of the most common frequency methods (discrete-cosine transform) is increasingly being replaced by a wavelet transform. This method of embedding confidential information is visually less noticeable to human vision, relative to those existing at a given time.The paper presents brief theoretical information on the wavelet transform, gives the characteristics of the Haar transform, presents images demonstrating the principle of the wavelet transform. The developed algorithm for steganography based on the wavelet transform is shown. The algorithm was implemented in the Matlab environment, as well as the analysis of the results obtained using the example of a test image. Conclusions, advantages of this method, as well as recommendations for further research in this area are given.
Keywords: Wavelet; Steganography; Image; Algorithm; Haar wavelet; Low passfilter; High passfilter; Approximation coefficients.
В. А. Варюхин (Национальный исследовательский университет ИТМО, Санкт-Петербург, Россия)
А. Б. Левина (Санкт-Петербургский государственный электротехнический университет «ЛЭТИ» им. В.И. Ульянова, Санкт-Петербург, Россия) E-mail:
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V. А. Variukhin (ITMO University, St. Petersburg, Russia)
A. В. Levina (Saint Petersburg Electrotechnical University "LETI", St. Petersburg, Russia) E-mail:
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1. Wu N., Hwang M. S. (2009). Data Hiding: Current Status and Key Issues. International Journal of Security and Networks, Vol. 4, (1), pp. 1 – 9.
2. Cheddad A. et al. (2010). Digital Image Steganography: Survey and Analysis of Current Methods. Signal Processing, Vol. 90, (3), pp. 727 – 752.
3. Akansu A., Haddad R. (2001). Wavelet Transform. Multiresolution Signal Decomposition, pp. 391 – 442. Academic Press.
4. Mallat S. (2009). Wavelet Bases. A Wavelet Tour of Signal Processing (Third Edition), pp. 263 – 376. Academic Press.
5. Naoki S. (2004). Frequently Asked Questions on Wavelets. University of California. Available at: https://www.math.ucdavis.edu/~saito/courses/ACHA.suppl/wavelet_faq.pdf (Accessed: 19.05.2021).
6. Nicolaou A. (1996). A Wavelet Wading Pool. Cornell University. Available at: http://www.cgl.uwater loo.ca/~anicolao/wadingpool/WaveletWadingPool.html (Accessed: 19.05.2021).
7. Levina A., Taranov S. (2016). Creation of Codes Based on Wavelet Transformation and its Application in ADV612 Chips. International Journal of Wavelets, Multiresolution and Information Processing, 15. Available at: https://www.researchgate.net/publication/ 311446869_Creation_of_Codes_Based_on_Wavelet_Transformation_and_its_Application_in_ADV612_Chips (Accessed: 19.05.2021).
8. Levina A., Taranov S. (2015). Algorithms of Constructing Linear and Robust Codes Based on Wavelet Decomposition and its Application. International Conference on Codes, Cryptology, and Information Security, 9084, pp. 247 – 258. Available at: https://www.research gate.net/publication/283882996_Algorithms_of_Constructing_Linear_and_Robust_Codes_Based_on_Wavelet_Decomposition_and_its_Application (Accessed: 19.05.2021).
9. Levina A., Taranov S. (2015). Construction of Linear and Robust Codes that is Based on the Scaling Function Coefficients of Wavelet Transforms. Journal ofApplied and Industrial Mathematics, (9), pp. 540 – 546. Available at: https://link.springer.com/article/ 10.1134/S1990478915040109 (Accessed: 19.05.2021).
10. Levina A., Taranov S. (2016). New Construction of Algebraic manipulation Detection Codes Based on Wavelet Transform. Proceedings of the 18th Conference of Open Innovations Association FRUCT, pp. 187 – 192. Saint Petersburg.
11. Ghasemi E., Jamshid S., Fassihi N. (2011). High Capacity Image Steganography using Wavelet Transform and Genetic Algorithm. Lecture Notes in Electrical Engineering, pp. 1 – 4. Hong Kong. Available at: http://www.iaeng.org/publication/IMECS2011/IMECS2011_pp495-498.pdf (Accessed: 19.05.2021)
12. Murugan G., Uthandipalayam R. (2020). Performance Analysis of Image Steganography using Wavelet Transform for Safe and Secured Transaction. Multimedia Tools and Applications, 79, pp. 9101 – 9115.
13. Hemalatha S., Acharya D., Renuka A. (2015). Wavelet Transform Based Steganography Technique to Hide Audio Signals in Image. Procedia Computer Science, 47, pp. 272 – 281.
14. Pramanik S., Bandyopadhyay S. (2014). Image Steganography Using Wavelet Transform And Genetic Algorithm. International Journal of Innovative Research in Advanced Engineering, Vol. 1, (1), pp. 1 – 4.
15. Avinash K., Madhuri S. (2015). An Image Steganography Method Hiding Secret Data into Coefficients of Integer Wavelet Transform Using Pixel Value Differencing Approach. Mathematical Problems in Engineering, (2), pp. 1 – 11. Available at: https://www.researchgate.net/publication/276374055_An_Image_Steganography_Method_Hiding_Secret_Data_into_Coefficients_of_Integer_Wavelet_Transform_Using_Pixel_Value_Differencing_Approach (Accessed: 19.05.2021).
16. Han-Yang L., Sanjeev T., Joyce W. (1998). Wavelet Based Steganography and Watermarking. Ithaca: Cornell University. Available at: https://www.cs. cor-nell.edu/topiwala/wavelets/report.html (Accessed: 19.05.2021).
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