A NOVEL PRIORITY BASED DOCUMENT IMAGE ENCRYPTION WITH MIXED CHAOTIC SYSTEMS USING MACHINE LEARNING APPROACH

Ravanna C R, Keshavamurthy C

DOI Number
10.2298/FUEE1901147R
First page
147
Last page
177

Abstract


Document images containing different types of information are required to be encrypted with different levels of security. In this paper, the image classification is carried out based on the feature extraction, for color images. The K-Nearest Neighbor (K-NN) method of image classification technique is used for classifying the query Document with trained set of features obtained from the Document database. Optical Character Recognition (OCR) technique is used to check for the presence as well as location of text/numerals in the Documents and to identify the Document type. Priority level is assigned in accordance with the Document type. Document images with different priorities are encrypted with different multi-dimensional chaotic maps. The Documents with different priority levels are diffused with different techniques. Document with highest priority are encrypted with highest level of security but Documents with lower priority levels are encrypted with lesser security levels. The proposed work was experimented for different document types with more number of image features for a large trained database. The results reveals a high speed of encryption for a set of document pages with priorities is more effective in comparison with a uniform method of encryption for all document types. The National Institute of Standards and Technology (NIST) statistical tests are also conducted to check for the randomness of the sequence and achieved good randomness. The proposed work also ensures security against the various statistical and differential attacks.


Keywords

Ikeda, Lorenz, Chaotic, Feature Space, NIST

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References


S. Marinai, Introduction to Document Analysis and Recognition. Springer-Verlag Berlin Heidelberg, 90, pp. 1-20, 2008.

A. Kumar, F. Nette, K. Klein, M. Fulham and J. Kim, “A visual Analytics Approach using the Exploration of Multi-Dimensional Feature Spaces fo Content- based Medical Image Retrieval”, IEEE Journal of Biomedical and Healt Informatics, pp. 168-2194, 2013.

N. Chen, D. Blostein, A survey of Document image Classification: Problem Statement, Classifier architecture and Performance Evaluation. Springer-IDJAR, 2006.

O. Augereaw, N. Journet, J-P. Domenger, “Document images Indexing with Relevance Feedback: an Application to Industrial Context”, In Proceedings of the International Conference on Document Analysis and Recognition, IEEE Computer Society, 2011, pp. 1190-1194.

F. Chen, A. Girgensohn, M. Cooper, Y. Lu and G. Filby, “Genre Identification for Office Document search and browsing. Springer-IDJAR”, 2012, pp. 167-182.

S. Sergyan, “Color Histogram Features Based Image Classification in Content- Based Image Retrieval Systems”, In Proceedings of the 6th International Symposium on Applied Machine Intelligence and Informatics, 2008.

F. Esposito, D. Malerba and F. A. Lisi, “Machine Learning for Intelligent Processing of Printed Documents”, Journal of Intelligent Information Systems, vol. 14, pp. 175-198, 2000.

V. Eglin, S. Bres, L.-Rfv, I. de Lyon, “Document page Similarity based on layout visual saliency: Application to query by example a Document Classification”, In Proceedings of the 7th International Conference on Document Analysis and Recognition (ICDAR-2003), IEEE-Computer Society, 2003.

A. Schenker, M. Last, H. Bunke and A. Kandel., “Classification of Web Documents using a Graph model”, In Proceedings of the 7th International Conference on Document Analysis and Recognition (ICDAR-2003), IEEE-Computer Society, 2003.

E. Appiani, F. Cesarini, A.M. Colla, M. Diligenti, M. Gori, S. Marinai and G. Soda, “Automatic Document Classification and Indexing in high-volume Applications”, Springer-Verlag (IJDAR), pp. 69-83, 2001.

Ms. K. Arthi and Mr. J. Vijayaraghavan, “Content based Image Retrieval Algorithm using Color Models”, International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, Issue 3, 2013.

S. Shastry, G. Gunasheela, T. Dutt, D. S. Vinay and S. R. Rupanagudi, ““i”-A novel algorithm for optical character Recognition (OCR)”, IEEE, pp. 389-393, 2013.

A. Farhat, A. Al-Zawqari, A. Al-Qahatni, O. Hommos, F. Bensaali, A. Amira and X. Zhai, “OCR Based Feature Extraction and Template Matching Algorithms for Qatari Number Plate”, IEEE, 2016.

C. R. Revanna and Dr. C Keshavamurthy, “A Secure Document Image Encryption Using Mixed Chaotic System” International Journal of Computer Science and Information Security (IJCSIS), vol. 15, no. 3, pp. 263-270, 2017.

C. R. Revanna and Dr. C Keshavamurthy, “A New Selective Document Image Encryption Using GMM-EM and Mixed Chaotic System”, International Journal of Applied Engineering Research, vol. 12, pp. 8854-8865, 2017.

H. Liu, and X. Wang, “Color image encryption using spatial bit-level permutation and High-dimension chaotic system’”, Optical Communication, vol. 284, pp. 3895–3903, 2011.

A. Melo, P. Bezerra, and A. Ablem, et al. “Priority QoE: a tool for Improving the QoE in Video Streaming”, Intelligent Multimedia Technologies for Networking Applications: Techniques and Tools, chapter 11.

N. Shaikh, S. Chapaneri and D. Jayaswal, “Hyper Chaotic Color Image Cryptosystem”, In Proceedings of the IEEE International conference on Advances in Computer Application, 2016, pp. 239-243.

V. Praneeth Kumar Reddy and A. Annis Fathima “A cost Effective Approach for Securing Medical X-ray images using Chebyshev Map”, In Proceedings of the IEEE 5th International Conference on Recent Trends in Information Technology, 2016.

M. Dridi, M. Ali Hajjaji, B. Bouallegue and A. Mtibaa, “Cryptography of medical images based on a combination between chaotic and neural Network”, IET, Image Processing, pp. 1-10, 2016.

B. Awdun and G. Li, “The Color Image Encryption Technology based on DNA Encoding and Sine Chaos”, In Proceedings of the IEEE International conference on Smart City and System Engineering. 539-544, 2016.

IEEE Computer Society. (1985). IEEE standard for binary Floating-Point Arithmetic, ANSI/IEEE standard, August 1985, p. 754.

G. Alvarez, and S.J. Li, “Some basic cryptographic requirements for chaos-based Cryptosystem”, Int. J. Bifurcation Chaos, vol. 16, no. 8, pp. 2129-2151, 2006.

G. Ye, “A block image encryption algorithm based on wave transmission and chaotic systems”, Springer-Nonlinear Dyn, vol. 75, pp. 417-427, 2014.

X. Wang, L. Liu, Y. Zhang, “A novel chaotic block image encryption algorithm based on dynamic random growth technique. ELSEVIER Optics and laser in Engineering, vol. 66, pp. 10-18, 2015.

Z. Yu, Z. Z. Yang et al., “A Chaos-Based Image Encryption Algorithm Using Wavelet Transform”, In Proceedings of the IEEE Conference, pp. 217-222, 2010.

D. E. Goumidi, F. Hachouf, “Hybrid chaos based image encryption approach using block and stream ciphers”, In Proceedings of the IEEE international workshop on system signal processing and their applications, 2013, pp.139-144.

S. M. Wadi and N. Zainal, High Definition Image Encryption Algorithm Based on AES Modification. Springer Science Business Media New York, pp. 811-829, 2014.

Y. Zhang, X. Li and W. Hou, “A Fast Image Encryption Scheme Based on AES”, In Proceedings of the 2nd International Conference on Image, Vision and Computing, 2017, pp. 624-628.

Y. Zhang, “Test and Verification of AES Used for Image Encryption”, Springer-Verlag GmbH Germany, 2018.


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