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Artificial Intelligence Review

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01.06.2016

A new data hiding method based on chaos embedded genetic algorithm for color image

verfasst von: Şengül Doğan

Erschienen in: Artificial Intelligence Review | Ausgabe 1/2016

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Abstract

Data hiding algorithms, which have many methods describing in the literature, are widely used in information security. In data hiding applications, optimization techniques are utilized in order to improve the success of algorithms. The genetic algorithm is one of the largely using heuristic optimization techniques in these applications. Long running time is a disadvantage of the genetic algorithm. In this paper, chaotic maps are used to improve the data hiding technique based on the genetic algorithm. Peak signal to noise ratio (PSNR) is chosen as the fitness function. Different sized secret data are embedded into the cover object using random function of MATLAB and chaotic maps. Randomness of genetic is performed by using different chaotic maps. The success of the proposed method is presented with comparative results. It is observed that gauss, logistic and tent maps are faster than random function for proposed data hiding method.

Vorheriger Artikel Hadith data mining and classification: a comparative analysis

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Alatas B (2010) Chaotic harmony search algorithms. Appl Math Comput 216:2687–2699MATH

Arsalan M, Malik SA, Khan A (2012) Intelligent reversible watermarking in integer wavelet domain for medical images. J Syst Softw 85(4):883–894CrossRef

Baykasoglu A (2012) Design optimization with chaos embedded great deluge algorithm. Appl Soft Comput 12:1055–1067CrossRef

Bender W, Paiz FJ, Butera W, Pogreb S, Gruhl D, Hwang R (2000) Applications for data hiding. IBM Syst J 39(3–4):547–568CrossRef

Bhowal K, Pal AJ, Tomar GS, Sarkar PP (2010) Audio steganography using GA. In: International conference on computational intelligence and communication networks, pp 449–453

Caponetto R, Fortuna L, Fazzino S, Gabriella M (2003) Chaotic sequences to improve the performance of evolutionary algorithms. IEEE Trans Evol Comput 7:289–304CrossRef

Chambers LD (2001) The practical handbook of genetic algorithms: applications. Chapman & Hall/CRC, Boca Raton

Chan CK, Cheng LM (2004) Hiding data in images by simple LSB substitution. Pattern Recogn 37(3):469–474CrossRefMATH

Chang CC, Lin CY, Fan YH (2008) Lossless data hiding for color images based on block truncation coding. Pattern Recogn 41:2347–2357CrossRefMATH

Chang CC, Hsieh YP, Lin CH (2008) Sharing secrets in stego images with authentication. Pattern Recogn 41(10):3130–3137CrossRefMATH

Cox IJ, Miller ML, Bloom JA, Fridrich J, Kalker T (2008) Digital watermarking and steganography. In: The Morgan Kaufmann series in multimedia information and systems

Dasgupta K, Mondal JK, Dutta P (2013) Optimized video steganography using genetic algorithm (GA). Procedia Technol 10:131–137CrossRef

Dr’eo J, P’etrowski A, Siarry P, Taillard E (2006) Metaheuristics for hard optimization. Springer, Berlin

Elattar EE (2015) A hybrid genetic algorithm and bacterial foraging approach for dynamic economic dispatch problem. Int J Electrical Power Energy Syst 69:18–26CrossRef

Elshoura SM, Megherbi DB (2013) A secure high capacity full-gray-scale-level multi-image information hiding and secret image authentication scheme via Tchebichef moments. Sig Process Image Commun 28:531–552CrossRef

El-Emam NN (2015) New data-hiding algorithm based on adaptive neural networks with modified particle swarm optimization. Comput Secur 55:21–45MathSciNetCrossRef

Ghebleh M, Kanso A (2014) A robust chaotic algorithm for digital image steganography. Commun Nonlinear Sci Numer Simul 19(6):1898–1907CrossRef

Haupt RL, Haupt SE (2004) Practical genetic algorithms. Wiley, New YorkMATH

Huang G, Cambria E, Toh K, Widrow B, Xu Z (2015) New trends of learning in computational intelligence. IEEE Comput Intell Mag 10(2):16–17CrossRef

Jawad K, Khan A (2013) Genetic algorithm and difference expansion based reversible watermarking for relational databases. J Syst Softw 86(11):2742–2753CrossRef

Kanan HR, Nazer B (2014) A novel image steganography scheme with high embedding capacity and tunable visual image quality based on a genetic algorithm. Expert Syst Appl 41:6123–6130CrossRef

Kanso A, Own HS (2012) Steganographic algorithm based on a chaotic map. Commun Nonlinear Sci Numer Simul 17(8):3287–3302MathSciNetCrossRef

Khan A, Malik SA, Ali A, Chamlawi R, Hussain M, Mahmood MT, Usman I (2012) Intelligent reversible watermarking and authentication: hiding depth map information for 3D cameras. Inf Sci 216:155–175CrossRef

Khan MK, Zhang J, Tian L (2007) Chaotic secure content-based hidden transmission of biometric templates. Chaos Solitons Fractals 32(5):1749–1759CrossRef

Khodaei M, Faez K (2010) Image hiding by using genetic algorithm and LSB substitution. Image Signal Process Lecture NotesComput Sci 6134:404–411CrossRef

Kim T, Lee K, Baik J (2015) An effective approach to estimating the parameters of software reliability growth models using a real-valued genetic algorithm. J Syst Softw 102:134–144CrossRef

Kuo RJ, Syu YJ, Chen ZY, Tien FC (2012) Integration of particle swarm optimization and genetic algorithm for dynamic clustering. Inf Sci 195:124–140CrossRef

Kurban T, Civicioglu P, Kurban R, Besdok E (2014) Comparison of evolutionary and swarm based computational techniques for multilevel color image thresholding. Appl Soft Comput 23:128–143CrossRef

L. University, UCID Image Dataset. http://homepages.lboro.ac.uk/~cogs/datasets/ucid/ucid.html. 18 Nov 2014

Li X, Wang J (2007) A steganographic method based upon JPEG and particle swarm optimization algorithm. Inf Sci 177(15):3099–3109CrossRef

Lin CC, Tsai WH (2004) Secret image sharing with steganography and authentication. J Syst Softw 73(3):405–414MathSciNetCrossRef

Liu Z, Zhang Y, Liu W, Meng F, Wu Q, Liu S (2013) Optical color image hiding scheme based on chaotic mapping and Hartley transform. Opt Lasers Eng 51(8):967–972CrossRef

Nian-Sheng L (2011) Pseudo-randomness and complexity of binary sequences generated by the chaotic system. Commun Nonlinear Sci Numer Simul 16(2):761–768MathSciNetCrossRefMATH

Roy R, Sarkar A, Changder S (2013) Chaos based edge adaptive image steganography. Procedia Technol 10:138–146CrossRef

Schaefer R (2007) Foundations of global genetic optimization. Springer, BerlinCrossRefMATH

Suneel M (2006) Chaotic sequences for secure CDMA, Ramanujan Institute for Advance Study in Mathematics, pp 1–4

Tataru RL, Battikh D, El Assad S, Noura H, Deforges O (2012) Enhanced adaptive data hiding in spatial LSB domain by using chaotic sequences. In: Eighth international conference on intelligent information hiding and multimedia signal processing (IIH-MSP), 2012, pp 85–88

Tu TY, Wang CH (2015) Reversible data hiding with high payload based on referred frequency for VQ compressed codes index. Sig Process 108:278–287CrossRef

Verma OP, Kumar P, Hanmandlu M, Chhabra S (2012) High dynamic range optimal fuzzy color image enhancement using artificial ant colony system. Appl Soft Comput 12(1):394–404CrossRef

Wang RZ, Lin CF, Lin JC (2001) Image hiding by optimal LSB substitution and genetic algorithm. Pattern Recogn 34(3):671–683CrossRefMATH

Wu CC, Kao SJ, Hwang MS (2011) A high quality image sharing with steganography and adaptive authentication scheme. J Syst Softw 84(12):2196–2207CrossRef

Yang CN, Chen TS, Yu KH, Wang CC (2007) Improvements of image sharing with steganography and authentication. J Syst Softw 80(7):1070–1076CrossRef

Yang D, Liu Z, Zhou J (2014) Chaos optimization algorithms based on chaotic maps with different probability distribution and search speed for global optimization. Commun Nonlinear Sci Numer Simul 19:1229–1246MathSciNetCrossRef

Zaki MR, Varshosaz J, Fathi M (2015) Preparation of agar nanospheres: comparison of response surface and artificial neural network modeling by a genetic algorithm approach. Carbohydr Polym 122:314–320CrossRef

Zamani M, Taherdoost H, Manaf AA, Ahmad RB, Zeki AM (2009) International conference on robust audio steganography via genetic algorithm, information and communication technologies ICICT ’09, pp 149–153

Zhang M, Tong X (2014) A new chaotic map based image encryption schemes for several image formats. J Syst Softw 98:140–154CrossRef

Zhang W, Ma K, Yu N (2014) Reversibility improved data hiding in encrypted images. Sig Process 94:118–127CrossRef

Titel: A new data hiding method based on chaos embedded genetic algorithm for color image
verfasst von: Şengül Doğan
Publikationsdatum: 01.06.2016
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence Review / Ausgabe 1/2016
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-016-9459-9

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