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An ensemble of monarchy butterfly optimization based encryption techniques on image steganography for data hiding in thermal images

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Abstract

Information security provides a process of hiding secret information to protect the sensitive data from attackers. To achieve secure data transmission, data hiding and steganographic techniques are being developed. Image steganography is commonly employed in several areas such as telecommunication, mobile computing, online voting system, image retrieval, and privacy of patient details. Furthermore, encryption technique acts as an integral part to save the original data from illegal access. In this view, this paper presents a new monarchy butterfly optimization algorithm-based encryption with image steganography for hiding information in thermal images (MBOA-ESTI). The presented MBOA-ESTI method includes various phases of operations like ensemble of encryption, channel extraction, optimal pixel selection, embedded process, and decomposition. Then, the encryption of the images takes place by using three encryption systems Rubik’s Cube Principle (RCP), Henon map (HM), and improved chaotic map (ICM). In addition, the presented MBOA-ESTI model involves multilevel discrete wavelet transform (DWT) based image decomposition process. In addition, the optimum pixel selection technique is performed using MBOA over the encrypted R, G, and B channels. For investigating the outcome of the MBOA-ESTI technique, wide-ranging experimentation was performed using MATLAB R2014a tool and the outcomes are examined interms of discrete performance measures. The experimental outcomes pointed out that the MBOA-ESTI model has resulted in an average mean square error (MSE) of 0.093, peak signal to noise ratio (PSNR) of 58.46dB, and normalized cross-correlation (NCC) of 0.997.

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References

  1. Ambika, Biradar RL, Burkpalli V (2019) Encryption-based steganography of images by multiobjective whale optimal pixel selection. Int J Comput Appl 44(4):1–10

    Google Scholar 

  2. Amuthadevi C, Vijayan DS, Ramachandran V. Development of air quality monitoring (AQM) models using different machine learning approaches. J Ambient Intell Humanized Comput. https://doi.org/10.1007/s12652-020-02724-2

  3. Atee HA, Ahmad R, Noor NM et al (2017) Extreme learning machine based optimal embedding location finder for image steganography. PLoS ONE 12(2):e0170329

    Article  Google Scholar 

  4. Daniel E et al (2017) Optimum spectrum mask based medical image fusion using gray wolf optimization. Biomed Signal Process Control 34:36–43

    Article  Google Scholar 

  5. Dhiman G (2021) ESA: a hybrid bio-inspired metaheuristic optimization approach for engineering problems. Eng Comput 37(1):323–353

    Article  Google Scholar 

  6. Dhiman G, Kumar V (2019) Seagull optimization algorithm: theory and its applications for large-scale industrial engineering problems. Knowl Based Syst 165:169–196

    Article  Google Scholar 

  7. Douglas M, Bailey K, Leeney M et al (2018) An overview of steganography techniques applied to the protection of biometric data. Multimedia Tools Appl 77(13):17333–17373

    Article  Google Scholar 

  8. Duan X, Song H, Qin C et al (2018) Coverless steganography for digital images based on a generative model. Comput Mater Continua 55(3):483–493

    Google Scholar 

  9. Hashim FA, Hussain K, Houssein EH, Mabrouk MS, Al-Atabany W (2021) Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Appl Intell 51(3):1531–1551

    Article  MATH  Google Scholar 

  10. Henon M (1976) A two-dimensional mapping with a strange attractor. The theory of chaotic attractors. Springer, New York, pp 94–102

    Chapter  Google Scholar 

  11. Hussain M, Wahab AWA, Idris YIB et al (2018) Image steganography in spatial domain: a survey. Signal Process Image Commun 65:46–66

    Article  Google Scholar 

  12. Kamil S, Abdullah SNHS, Hasan MK, Bohani FA (2021) Enhanced flipping technique to reduce variability in Image Steganography. IEEE Access 9:168981–168998

    Article  Google Scholar 

  13. Kaur A, Kaur B (2015) Exploring the techniques of data embedding in images: a review. Int J Comput Appl 122(3):18–21

    Google Scholar 

  14. Li M, Wang P, Liu Y, Fan H (2019) Cryptanalysis of a novel bit-level color image encryption using improved 1D chaotic map. IEEE Access 7:145798–145806

    Article  Google Scholar 

  15. Loukhaoukha K, Chouinard JY, Berdai A (2012) A secure image encryption algorithm based on Rubik’s cube principle. J Electr Comput Eng 2012:1–13

  16. Mandal PC, Mukherjee I (2022) High capacity data hiding based on multi-directional pixel value differencing and decreased difference expansion. Multimedia Tools Appl 81(4):5325–5347

    Article  Google Scholar 

  17. Manjunatha Reddy HS, Raja KB (2009) High capacity and security steganography using discrete wavelet transform. Int J Comput Sci Secur 4(3):462–472

    Google Scholar 

  18. Melman A, Evsutin O (2021) On the efficiency of metaheuristic optimization for adaptive image steganography in the dft domain. In: 2021 XVII International Symposium” Problems of Redundancy in Information and Control Systems“(REDUNDANCY). IEEE, pp 49–54

  19. Priyanka BG, Sathyanarayana SV (2014) A steganographic system for embedding image and the encrypted text. In: 2014 International Conference on Contemporary Computing and Informatics (IC3I), Mysore, India. IEEE, pp 1351–1355

  20. Rajput SS, Bohat VK, Arya KV (2019) Grey wolf optimization algorithm for facial image super-resolution. Appl Int 49(4):1324–1338

    Article  Google Scholar 

  21. Rathika S, Gayathri R (2021) Performance analysis of data hiding in thermal image using alpha blending technique. Mater Today: Proc 46:10164–10168

  22. Sheela SJ, Suresh KV, Tandur D (2018) Image encryption based on modified Henon map using hybrid chaotic shift transform. Multimedia Tools Appl 77(19):25223–25251

    Article  Google Scholar 

  23. Sun L, Chen S, Xu J, Tian Y (2019) Improved monarch butterfly optimization algorithm based on opposition-based learning and random local perturbation. Complexity 2019:1–20

  24. Teja JD, Rao ACS, Dara S (2017) A new image steganography technique for hiding the data in multi layers of the PNG images. Int J Ad Hoc Ubiquit Computes 2(7):104–112

    Google Scholar 

  25. Thanki R, Borra S (2018) A color image steganography in hybrid FRT–DWT domain. J Inf Secur Appl 40:92–102

    Google Scholar 

  26. Vatsa M, Singh R, Noore A, Houck MM, Morris K (2006) Robust biometric image watermarking for fingerprint and face template protection. IEICE Electron Express 3(2):23–28

    Article  Google Scholar 

  27. Vijayan DS, Leema Rose A, Arvindan S, Revathy J, Amuthadevi C. Automation systems in smart buildings: a review. J Ambient Intell Humanized Comput. https://doi.org/10.1007/s12652-020-02666-9

  28. Wang S, Cao Z, Strangio MA, Wang L (2008) Cryptanalysis and improvement of an elliptic curve Diffie-Hellman key agreement protocol. IEEE Commun Lett 12(2):149–151

    Article  Google Scholar 

  29. Wang R-Z, Lin C-F, Lin J-C (2000) Hiding data in images by optimal moderately-significant-bit replacement. Electron Lett 36(25):2069–2070

    Article  Google Scholar 

  30. Zhou Z, Yang C-N, Cao Y et al (2018) Secret image sharing based on encrypted pixels. IEEE Access 6:15021–15025

    Article  Google Scholar 

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  1. Department of Electronics and Communication Engineering, Annamalai University, Chidambaram, Tamil Nadu, 608002, India

    S. Rathika & R. Gayathri

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  1. S. Rathika
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  2. R. Gayathri
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Rathika, S., Gayathri, R. An ensemble of monarchy butterfly optimization based encryption techniques on image steganography for data hiding in thermal images. Multimed Tools Appl 82, 47235–47252 (2023). https://doi.org/10.1007/s11042-023-15693-3

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