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Selection of Image Blocks using Genetic Algorithm and Effective Embedding with DCT for Steganography

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Published:21 October 2016Publication History

ABSTRACT

In today's digital world that we live in, security of information is crucial in various communication applications that are widely developed. Steganography is one of the highly secure information hiding techniques. It provides invisible communication and hides the existence of information. This paper focuses on 'before embedding technique' of hiding in image steganography by trying to find suitable places in cover image to embed the secret image. Genetic algorithm (GA) is applied to identify appropriate places in cover image where embedding of secret image will cause minimum distortion. After obtaining these places, embedding is performed using transform domain technique Discrete Cosine Transform (DCT). The secret image is first normalized and then embedded in the lower energy DCT blocks of the selected cover image regions. The experimental results show that the stego images obtained from the proposed method have less visual distortion with satisfactory values in parameters like MSE, PSNR and Correlation used for performance evaluation.

References

  1. Jan HP Eloff, Martin S. Olivier and Tayana Morkel. An overview of image steganography. In Proceedings of the Fifth Annual Information Security South Africa Conference (ISSA), 2005.Google ScholarGoogle Scholar
  2. Bagheri Baba Ahmadi. Image Watermarking: Blind Linear Correlation Technique. World Applied Programming, pages 93--100, 2015.Google ScholarGoogle Scholar
  3. Mansi S. Subhedara and Vijay H. Mankarb. Current status and key issues in image steganography: A survey. Computer Science Review, October 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. S. G. Shelke and S. K. Jagtap. A Novel Approach: Pixel Matching Based Image Steganography. In International Conference on Pervasive Computing (ICPC), pages 1--4, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  5. Ali Hanani, Masoud Nosrati and Ronak Karimi. Steganography in Image Segments using Genetic Algorithm. In Fifth IEEE International Conference on Advanced Computing & Communication Technologies (ACCT), pages 102--107, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. M. Nosrati and R. Karimi. A Survey on Usage of Genetic Algorithms in Recent Steganography Researches. World Applied Programming, pages 206--210, 2012.Google ScholarGoogle Scholar
  7. L. H. Chen and Y. K. Lee. High capacity image steganographic model. In IEEE Proceedings-Vision, Image and Signal Processing, 147(3):288--294, June 2000.Google ScholarGoogle ScholarCross RefCross Ref
  8. D. C. Wu and W. H. Tsai. A steganographic method for images by pixel value differencing. Pattern Recognition Letters, pages 1613--1626, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. E. Chang and V. Potdar. Gray level modification steganography for secret communication. In 2nd IEEE International Conference on Industrial Informatics, pages 355--368, May 2004.Google ScholarGoogle Scholar
  10. Chi-Kwong Chan and L. M. Cheng. Improved hiding data in images by optimal moderately-significant-bit replacement. Electronics Letters, pages 1017--1018, 2001.Google ScholarGoogle ScholarCross RefCross Ref
  11. S. Wang and X. Zhang. Efficient Steganographic embedding by exploiting modification direction. IEEE Communications Letters, 10(11):781--783, November 2006.Google ScholarGoogle ScholarCross RefCross Ref
  12. C. Lee, H. Wu, R. Chao and Y. Chu. A novel image data hiding scheme with diamond encoding. EURASIP Journal on Information Security, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. "Lempel-Ziv-Welch (LZW) Compression," Source: http://www.fileformat.info/mirror/egff/ch09_04.htmGoogle ScholarGoogle Scholar
  14. Kolsoom Shahryari and Mehrdad Gholami. High Capacity Secure Image Steganography Based on Contourlet Transform. Advances in Computer Science: an International Journal, 2(4):62--65, September 2013.Google ScholarGoogle Scholar
  15. Bian Yang, Shen Wang and Xiamu Niu. A Secure Steganography Method based on Genetic Algorithm. Journal of Information Hiding and Multimedia Signal Processing, 1(1):28--35, January 2010.Google ScholarGoogle Scholar
  16. E. Baburaj and P.M. Siva Raja. Data Hiding Scheme For Digital Images Based on Genetic Algorithms with LSBMR. International Journal of Computer Applications, 59(5):8--15, December 2012.Google ScholarGoogle ScholarCross RefCross Ref
  17. Elham Ghasemi, Jamshid Shanbehzadeh and Nima Fassihi. High Capacity Image Steganography based on Genetic Algorithm and Wavelet Transform. Intelligent Control and Innovative Computing, Springer US, pages 395--404, 2012.Google ScholarGoogle Scholar
  18. Bahram Nazeri and Hamidreza Rashidy Kanan. A novel image steganography scheme with high embedding capacity and tunable visual image quality based on a genetic algorithm. Expert Systems with Applications, pages 6123--6130, October 2014.Google ScholarGoogle Scholar
  19. "Standard Deviation Formulas," Source: https://www.mathsisfun.com/data/standard-deviation-formulas.htmlGoogle ScholarGoogle Scholar
  20. H. B. Kekre, Rekha Vig and Tanuja Sarode. Multi-resolution Analysis of Multi-spectral Palmprints using Hybrid Wavelets for Identification. International Journal of Advanced Computer Science and Applications (IJACSA), 2013.Google ScholarGoogle Scholar
  21. Dr. Der Chen Soong and Yusra A. Y. Al-Najjar. Comparison of Image Quality Assessment: PSNR, HVS, SSIM, UIQI. International Journal of Scientific & Engineering Research, August 2012.Google ScholarGoogle Scholar
  22. Joseph Lee Rodgers and W. Alan Nicewander. Thirteen ways to look at the correlation coefficient. The American Statistician, 42(1):59--66, February 1988.Google ScholarGoogle Scholar
  23. Son, S.-W., C. Christensen, P. Grassberger, and M. Paczuski. PageRank and rank-reversal dependence on the damping factor. Physical Review E, 2012.Google ScholarGoogle Scholar
  24. Gautam Sanyal, Indradip Banerjee and Souvik Bhattacharyya. Robust image steganography with pixel factor mapping (PFM) technique. In IEEE International Conference on Computing for Sustainable Global Development (INDIACom), pages 692--698, 2014.Google ScholarGoogle Scholar
  25. "Interpret the key results for Correlation," Source: http://support.minitab.com/en-us/minitab-express/1/help-and-how-to/modeling-statistics/regression/how-to/correlation/interpret-the-results/Google ScholarGoogle Scholar
  1. Selection of Image Blocks using Genetic Algorithm and Effective Embedding with DCT for Steganography

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      • Published in

        cover image ACM Other conferences
        COMPUTE '16: Proceedings of the 9th Annual ACM India Conference
        October 2016
        178 pages
        ISBN:9781450348089
        DOI:10.1145/2998476

        Copyright © 2016 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 21 October 2016

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        Acceptance Rates

        COMPUTE '16 Paper Acceptance Rate22of117submissions,19%Overall Acceptance Rate114of622submissions,18%

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