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LSB based steganography with OCR: an intelligent amalgamation

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Abstract

Steganography, the art of hiding information within information, has an added advantage over cryptography as the person viewing the object in which the information is hidden has no knowledge of the presence of any hidden information. The Least Significant Bit (LSB) embedding technique which is one of the widely used techniques ensures an indiscernible change in the cover image oblivious to the human eye. In this paper, an Optical Character Recognition (OCR) based Steganographic technique is introduced, in which message, in its feature form, is embedded in the cover image. We extract character level features from images which contain the textual message, and embed these features in the cover image, strengthening the data hiding objective of steganography. This is because an intruder has to know about the presence of features as hidden bits and even thereafter has to have trained OCR model to retrieve the texts from the decoded information i.e. from the features. We validate our results on an English Printed Character dataset (Chars74K Dataset), and present the evaluation results for varied LSBs.

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References

  1. Areepongsa S, Kaewkammerd N, Syed YF, Rao KR (2000) Exploring on steganography for low bit rate Wavelet based coder in image retrieval system. Proceedings of IEEE TENCON, pp 250–255

  2. Avola D, Buono AD, Gianforme G, Paolozzi S, Wang R (2009) Sketchml a representation language for novel sketch recognition approach. In: Proceedings of the 2nd international conference on Pervasive Technologies Related to Assistive Environments, PETRA 2009. ACM, New York, pp 1–8

    Google Scholar 

  3. Avola D, Bernardi M, Cinque L, Foresti GL, Marini MR, Massaroni C (2017) A machine learning approach for the online separation of handwriting from freehand drawing. In: Battiato S, Gallo G, Schettini R, Stanco F (eds) Image analysis and processing – ICIAP 2017, Lecture Notes in Computer Science, vol 10484. Springer, Cham

    Google Scholar 

  4. Avola D, Bernardi M, Cinque L et al (2019) Multimed Tools Appl. https://doi.org/10.1007/s11042-019-7196-1

  5. Bai J et al (2017) A high payload steganographic algorithm based on edge detection. Displays 46:42–51

    Article  Google Scholar 

  6. Boehm B (2014) Stegexpose-A tool for detecting LSB steganography. arXiv preprint arXiv:1410.6656

  7. Charles PK et al (2012) A review on the various techniques used for optical character recognition. Int J Eng Res Appl 2(1):659–662

    Google Scholar 

  8. Chaudhuri BB, Pal U (1998) A complete printed Bangla OCR system. Pattern Recogn 31(5):531–549

    Article  Google Scholar 

  9. de Campos T, Babu BR, Varma M (2009) Character recognition in natural images. http://personal.ee.surrey.ac.uk/Personal/T.Decampos/papers/decampos_etal_visapp2009.pdf

  10. Dumitrescu S, Wu X, Wang Z (2002) Detection of LSB steganography via sample pair analysis. In: International workshop on information hiding. Springer, Berlin/Heidelberg

    Google Scholar 

  11. Dumitrescu S, Wu X, Memon N (2002) On steganalysis of random LSB embedding in continuous-tone images. Proceedings of international conference on image processing, vol 3. IEEE

  12. Elhadad A (2019) Soft Comput. https://doi.org/10.1007/s00500-019-04041-z

  13. Elhadad A, Hamad S, Khalifa A et al (2017) Neural Comput Applic 28(Suppl 1):91. https://doi.org/10.1007/s00521-016-2323-7

    Article  Google Scholar 

  14. Fridrich J, Goljan M, Rui D (2001) Detecting LSB steganography in color, and gray-scale images. IEEE Multimedia 8(4):22–28

    Article  Google Scholar 

  15. Garg T, Joshi K, Pandey J (2018) Enhancement of efficiency in LSB steganography method using matrix multiplication. Int J Comput Sci Eng 6:1267–1278

    Google Scholar 

  16. Ghazanfari K, Ghaemmaghami S, Khosravi SR (2011) LSB++: an improvement to LSB+ steganography. TENCON 2011–2011 IEEE Region 10 conference. IEEE

  17. Hallouli K, Likforman-Sulem L, Sigelle M (2002) A comparative study between decision fusion and data fusion in Markovian printed character recognition. Pattern Recognition, 2002. Proceedings of 16th international conference on, vol 3. IEEE

  18. Hetzl S, Mutzel P (2005) A graph–theoretic approach to steganography. In: IFIP international conference on communications and multimedia security. Springer, Berlin/Heidelberg

    Google Scholar 

  19. Hinton GE, Williams CKI, Revow MD (1992) Adaptive elastic models for hand-printed character recognition. Adv Neural Inf Proces Syst

    Google Scholar 

  20. https://data.mendeley.com/datasets/sp4g8h7v8k/1. Accessed on 8 Oct 2019

  21. Hussain M, Abdul Wahab AW, Javed N, Jung K-H (2016) Hybrid data hiding scheme using right-most digit replacement and adaptive least significant bit for digital images. Symmetry 8:41

    Article  MathSciNet  Google Scholar 

  22. Johnson NF, Jajodia S (1998) Exploring steganography: seeing the unseen. Computer 31(2):26–34

    Article  Google Scholar 

  23. Joshi K, Yadav R, Chawla G (2017) An enhanced method for data hiding using 2 bit XOR in image steganography. Int J Eng Technol 8(6):3043–3055

    Article  Google Scholar 

  24. Jung KH, Yoo KY (2014) Steganographic method based on interpolation and LSB substitution of digital images. Springer Science+Business Media 74:2143–2155

    Google Scholar 

  25. Kawaguchi E, Eason RO (1998) Principle and applications of bpcs steganography. In: Multimedia systems and applications, vol 3528, pp. 464–473, SPIE

  26. Kruus P, Scace C, Heyman M, Mundy M (2003) A survey of steganography techniques for image files. Adv Secur Res J [On line] 5(1):41–52 Available: http://www.isso.sparta.com/documents/asrjv5.pdf#page=47 [Oct., 2011]

  27. Kumar M et al (2018) Character and numeral recognition for non-Indic and Indic scripts: a survey. Artif Intell Rev 52:1–27

    Google Scholar 

  28. Lorigo LM, Govindaraju V (2006) Offline Arabic handwriting recognition: a survey. IEEE Trans Pattern Anal Mach Intell 28(5):712–724

    Article  Google Scholar 

  29. Mielikainen J (2006) LSB matching revisited. IEEE Signal Process Lett 13(5):285–287

    Article  Google Scholar 

  30. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66

    Article  Google Scholar 

  31. Patil V, Shimpi S (2011) Handwritten English character recognition using neural network. Elixir Comput Sci Eng 41:5587–5591

    Google Scholar 

  32. Sahu VL, Kubde B (2013) Offline handwritten character recognition techniques using neural network: a review. Int J Sci Res (IJSR) 2(1):87–94

    Google Scholar 

  33. Sallee P (2004) Model-based steganography. In: Proceedings of the 2nd international workshop on digital watermarking. LNCS, pp 254–260

  34. Shah PD, Bichkar RS (2018) A secure spatial domain image steganography using genetic algorithm and linear congruential generator. International conference on intelligent computing and applications. Springer, Singapore

    Google Scholar 

  35. Trier ØD, Jain AK, Taxt T (1996) Feature extraction methods for character recognition- a Survey. Pattern Recogn 29(4):641–662

    Article  Google Scholar 

  36. Wu D-C, Tsai W-H (2003) A steganographic method for images by pixel-value differencing. Pattern Recogn Lett 24(9–10):1613–1626

    Article  Google Scholar 

  37. Wu H-T, Dugelay J-L, Cheung Y-M (2008) A data mapping method for steganography and its application to images. 10th international workshop on information hiding, vol 5284, pp 236–250, USA, May 2008

  38. Yang C-S, Yang Y-H (2017) Improved local binary pattern for real scene optical character recognition. Pattern Recogn Lett 100:14–21

    Article  Google Scholar 

  39. Yuan H-D (2014) Secret sharing with multi-cover adaptive steganography. Inf Sci 254:197–212

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, 188, Raja S. C. Mallick Road, Kolkata, West Bengal, Pin -700032, India

    Agneet Chatterjee & Ram Sarkar

  2. Department of Computer Science & Technology, Nalhati Government Polytechnic, Birbhum, Pin-731243, India

    Sudipta Kumar Ghosal

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  1. Agneet Chatterjee
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  2. Sudipta Kumar Ghosal
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  3. Ram Sarkar
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Correspondence to Ram Sarkar.

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Chatterjee, A., Ghosal, S.K. & Sarkar, R. LSB based steganography with OCR: an intelligent amalgamation. Multimed Tools Appl 79, 11747–11765 (2020). https://doi.org/10.1007/s11042-019-08472-6

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