USING GENETIC ALGORITHMS TO FIND INVERSE PSEUDO-RANDOM BLOCK PERMUTATIONS
DOI:
https://doi.org/10.26906/SUNZ.2019.4.072Keywords:
genetic algorithms, inverse pseudo-random block permutation, permuted image, (pseudo) holographic coding, correlation radiusAbstract
The subject matter of the article is a method for finding inverse pseudorandom block permutations of pixels in an image. The goal is to develop a "blind" method for finding inverse pseudo-random block permutations using genetic algorithms. Tasks: to analyze the factors affecting inverse pseudorandom block permutations in the image, to develop a method for coding permutations in genetic algorithms, to justify the choice of the objective function for optimization using genetic algorithms. The methods used are: methods of digital image processing, probability theory, mathematical statistics, cryptography and information protection, the mathematical apparatus of matrix theory.The following results are obtained. The analysis of factors affecting inverse pseudorandom block permutations in the image is carried out. The factors affecting the maximum block size at which the inverse permutation is still possible are determined. A method has been developed for finding inverse pseudorandom block permutations of pixels in a permuted image using genetic algorithms. Conclusions. The scientific novelty of the results is as follows.It was established that finding inverse permutations is possible only on condition that the block size is smaller than the image correlation radius. An effective method for coding permutations is proposed, in which the standard operators of genetic algorithms will generate new and only permissible permutations. It is proposed to use the sum of the squared gradients as the objective function.It is shown that this objective function has a global minimum for correct permutation, which allows one to find inverse block permutations "blindly" without additional a priori information.Downloads
References
S. Skiena, S. The Algorithm Design Manual / Steven S. Skiena // Second Edition. Springer. – 2008.
Salleh, M. Image Encryption Algorithm Based on Chaotic Mapping / M. Salleh, S. Ibrahim, I. FauziIsninl //JurnalTeknologi. – 2003.
Mitra, A. A new image encryption approach using combinational permutation techniques / A. Mitra, V. Y. SubbaRao, R. S. MahadevaPrasanna // International Journal of Electrical and Computer Engineering. – 2006.
Rhouma, R. Cryptoanalysis of a new image encryption algorithm based on hyper-chaos /R. Rhouma, B. Safya// Phyiscal Letters A. – 2008. – Vol. 372. – P. 5973-5978.
Al-Najjar,H . Digital Image Encryption Algorithm Based on a Linear Independence Scheme and the Logistic Map / H. alNajjar// International Arab Conference on Information Technology (ACIT 2011). – 2011.
Holland, J. Adaptation in Natural and Artificial Systems / J. Holland // Cambridge, MA: MIT Press. – 1992. – ISBN 978- 0262581110.
Mitchell, M. An Introduction to Genetic Algorithms / M. Mitchell // Cambridge, MA: MIT Press. – 1996. – ISBN 9780585030944.
Banzhaf, W. Genetic Programming – An Introduction / W. Banzhaf, P. Nordin, R. Keller, F.Francone // San Francisco, CA: Morgan Kaufmann. –1998. –ISBN 978-1558605107.
G. Jacob. Genetic Algorithm based Mosaic Image Steganography for Enhanced Security /George, Jacob and Janahanlal Stephen. – 2014.
Evolutionary Algorithms for Feature Selection, [Електронний ресурс]. – Режим доступу до ресурсу: https://www.kdnuggets.com/2017/11/rapidminer-evolutionary-algorithms-feature-selection.html. звернення 2018-02-19).
Dos Santos-Paulino, A. Evolutionary algorithm for dense pixel matching in presence of distortions / A. dos Santos-Paulino, J.-C. Nebel, F.Florez-Revuelta //EvoStar Conference, Granada, Spain. – 23–25 April 2014.
Paulinas, Mantas. A survey of genetic algorithms applications for image enhancement and segmentation / Paulinas, Mantas and Usinskas, Andrius // Information Technology and Control. – 2007. – Vol. 36. – P. 278-284.
Behzadi, S. Developing a Genetic Algorithm to Solve Shortest Path Problem on a Raster Data Model / S. Behzadi, Ali A. Alesheikh, E. Poorazizi //Journal of Applied Sciences. – 2008. – N. 8. –P. 3289-3293.
Cagnoni, Stefano. Genetic and evolutionary computation for image processing and analysis / Cagnoni, Stefano &Lutton, Evelyne&Olague, Gustavo. – 2007.
Bruckstein, A.M. Holographic representation of images / A.M. Bruckstein, R.J. Holt, A.N. Netravali // IEEE Transactions on Image Processing. – 1998. – N 7. – P. 1583-1587.
Bruckstein, A.M. Holographic image representations: the subsampling method / A.M. Bruckstein, R.J. Holt, A.N. Net-ravali // IEEE Int. Conference on Image Processing – Santa Barbara, California, USA, October. – 1997. – Vol. 1. – P. 177-180.
Bruckstein, A.M. Technique for Holographic Representation of Images / A.M. Bruckstein, R.J. Holt, A.N. Netravali // US 6,091,394. – July 18, 2000. – 6 p.
Колесов, В.В. Псевдоголографическое кодирование цифровой информации / В.В. Колесов, Н.Н. Залогин, Г.М. Воронцов // Радиотехника и электроника. – 2002. – Т. 2, № 5. – С. 583-588.
Марковский, А.В. О квазиголографическом кодировании цифровых изображений / А.В. Марковский // Автоматика и телемеханика. – 2001. – № 9. – С. 163-173.
Кузнецов, О.П. Квазиголографический подход к кодированию графической информации / О.П. Кузнецов, А.В. Марковский // Искусственный интеллект. – 2002. – № 2. – С. 474-482.
Баринова, Д.А. Разработка и исследование алгоритмов обработки цифровых изображений, представленных в псевдоголографических кодах / Д.А. Баринова // Компьютерная оптика. – 2005. – № 27. – С. 149-154.
Воронин, В.В. Голографическое представление в задачах обработки изображений / В.В. Воронин // Тезисы конференции РОАИ – 5. – 2000. – С. 237-241.
Yaloveha V., Hlavcheva D., Podorozhniak A. Usage of convolutional neural network for multispectral image processing applied to the problem of detecting fire hazardous forest areas. Сучасні інформаційні системи. 2019. Т. 3, № 1. С. 116– 120. DOI: https://doi.org/10.20998/2522-9052.2019.1.19.
Kuchuk G., Kovalenko A., Komari I.E., Svyrydov A., Kharchenko V. Improving big data centers energy efficiency: Traffic based model and method. Studies in Systems, Decision and Control, vol 171. Kharchenko, V., Kondratenko, Y., Kacprzyk, J. (Eds.). Springer Nature Switzerland AG, 2019. Pp. 161-183. DOI: http://doi.org/10.1007/978-3-030-00253-4_8
Amin Salih Mohammed, Saravana Balaji B., Hiwa Abdulkarim Mawlood. Conceptual analysis of Iris Recognition Systems. Advanced Information Systems. 2019. Vol. 3, No. 2. Р. 86-90. DOI : https://doi.org/10.20998/2522-9052.2019.2.15
Freeman, H. .Apictorial Jigsaw Puzzles: The Computer Solution of a Problem in Pattern Recognition / H. Freeman, L. Garder // IEEE Transactions on Electronic Computers, EC-13(2): – 1964. – P. 118-127.
Cho, T. A Probabilistic Image Jigsaw Puzzle Solver / T. Cho, S. Avidan, W. Freeman // Int. IEEE Conference on Computer Vision and Pattern Recognition. – 2010. – P. 183-190.
Yang, X. Particle Filter with State Permutations for Solving Image Jigsaw Puzzles / X. Yang, N. Adluru, L. J. Latecki // Int. IEEE Conference on Computer Vision and Pattern Recognition. – 2011. –P. 2873–2880.
Pomeranz, D. A Fully Automated Greedy Square Jigsaw Puzzle Solver / D. Pomeranz, M. Shemesh, and O. Ben-Shahar // In IEEE Conference on Computer Vision and Pattern Recognition. – 2011. – P. 9-16.
Sholomon, D. A Genetic AlgorithmBased Solver for Very Large Jigsaw Puzzles / D. Sholomon, O. David, N. Netanyahu // Int. 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). – 2013. – P. 1767-1774.
MATLAB в імплементації функції randperm, [Електронний ресурс]. – Режим доступу до ресурсу: https://www.mathworks.com/help/matlab/ref/randstream.randperm.html. звернення 2018-04-03).
Li, X. An efficient two dimensional moving average model for texture analysis and synthesis / X. Li, J.A. Cadzow, D.M. Wilkes, R.A. Peters, M. Bodruzzaman //Publication Year. – 1992. – Vol. 1. –P. 392-395.
Красильщиков М.Н., Себряков Г.Г. (ред.). Современные информационные технологии в задачах навигации и наведения беспилотных маневренных летательных аппаратов. М.: Физматлит, 2009. – 556 с. – ISBN: 978-5-9221-1168-3.
MATLAB 9.4.0.813654 (R2018a) з використанням Global Optimization Toolbox Version 3.4.4, [Електронний ресурс]. – Режим доступу до ресурсу: https://www.mathworks.com/matlabcentral/answers/404947-using-syms-matlab-stopsworking. звернення 2018-04-19).
Adobe Acrobat Reader DC [Електронний ресурс]. – Режим доступу до ресурсу: https://www.adobe.com/. звернення 2018-05-10).
