Data hiding in digital images using a partial optimization technique based on the classical LSB method

This paper presents a new partial optimization approach for the least significant bit (LSB) data hiding technique that can be used for protecting any secret information or data. A deterioration effect of as little as possible in an image is intended using the LSB data hiding technique and this is well realized utilizing the proposed partial optimization approach achieving the same data embedding bit rates. In the proposed approach, all of the image pixels are classified into 8 regions and then the 8 distinct ordering codings are applied to each region by the developed partial optimization encoder. Thus, the most effective outcome that has been obtained from the 8 regions means that the number of the altered bits is kept minimized. Hence, the minimal values that have been attained from the 8 regions enable decoding that ensures relatively small distortions on the extracted cover image.

Data hiding in digital images using a partial optimization technique based on the classical LSB method

This paper presents a new partial optimization approach for the least significant bit (LSB) data hiding technique that can be used for protecting any secret information or data. A deterioration effect of as little as possible in an image is intended using the LSB data hiding technique and this is well realized utilizing the proposed partial optimization approach achieving the same data embedding bit rates. In the proposed approach, all of the image pixels are classified into 8 regions and then the 8 distinct ordering codings are applied to each region by the developed partial optimization encoder. Thus, the most effective outcome that has been obtained from the 8 regions means that the number of the altered bits is kept minimized. Hence, the minimal values that have been attained from the 8 regions enable decoding that ensures relatively small distortions on the extracted cover image.

___

  • T. Shanableh, “Data hiding in mpeg video files using multivariate regression and flexible macroblock ordering”, IEEE Transactions on Information Forensics and Security, Vol. 7, pp. 455–464, 2012.
  • W. Hong, T.S. Chen, “A novel data embedding method using adaptive pixel pair matching”, IEEE Transactions on Information Forensics and Security, Vol. 7, pp. 176–184, 2012.
  • Y. Yalman, F. Akar, I. Erturk, “An image interpolation based reversible data hiding method using r-weighted coding”, 13th IEEE International Conference on Computational Science and Engineering, pp. 346–350, 2010.
  • Y. Yalman, I. Erturk, “A new histogram modification based robust image data hiding technique”, 24th IEEE International Symposium on Computer and Information Sciences, pp. 39–43, 2009.
  • E. Elba¸si, “Robust multimedia watermarking: hidden Markov model approach for video sequences”, Turkish Journal of Electrical Engineering & Computer Sciences, Vol. 18, pp. 159–170, 2010.
  • S.M.M. Karim, M.S. Rahman, M.I. Hossain, “A new approach for LSB based image steganography using secret key”, 14th IEEE International Conference on Computer and Information Technology, pp. 286–191, 2011.
  • K. Ghazanfari, S. Ghaemmaghami, S.R. Khosravi, “LSB ++ : an improvement to LSB + steganography”, IEEE Region 10 Conference: Tencon 2011, pp. 364–368, 2011.
  • Y. Qiudong, X. Liu, “A new LSB matching steganographic method based on steganographic information table”, 2nd IEEE International Conference on Intelligent Networks and Intelligent Systems, pp. 362–365, 2009.
  • C.H. Yang, C.Y. Weng, S.J. Wang, H.M. Sun, “Adaptive data hiding in edge areas of images with spatial LSB domain systems”, IEEE Transactions on Information Forensics and Security, Vol. 3, pp. 488–497, 2008.
  • F. Akar, Implementation of Information Security Based on Steganography and Cryptology, PhD, Marmara University, 2005.
  • H.T. Sencar, M. Ramkumar, A.N. Akansu, Data Hiding Fundamentals and Applications, New York, Elsevier Academic Press, 2004.
  • C.C. Chang, C.C. Lin, Y.H. Chen, “Reversible data-embedding scheme using differences between original and predicted pixel values”, IET Information Security, Vol. 2, pp. 35–46, 2008.
  • A.N. Netravali, B.G. Haskell, Digital Pictures: Representation, Compression, and Standards, New York, Plenum Press, 1995.
  • M. Rabbani, P.W. Jones, Digital Image Compression Techniques, Washington DC, SPIE Optical Engineering Press, 19 O. Cetin, A.T. Ozcerit, “A new steganography algorithm based on color histograms for data embedding into raw video streams”, Computers & Security, Vol. 28, pp. 670–682, 2009.
  • Z. Wang, A.C. Bovik, “A universal image quality index”, IEEE Signal Processing Letters, Vol. 9, pp. 81–84, 2002. K. Egiazarian, J. Astola, N. Ponomarenko, V. Lukin, F. Battisti, M. Carli, “A new full-reference quality metrics based on HVS”, CD-ROM Proceedings of the 2nd International Workshop on Video Processing and Quality Metrics, 200
  • N. Ponomarenko, F. Silvestri, K. Egiazarian, M. Carli, J. Astola, V. Lukin, “On between-coefficient contrast masking of DCT basis functions”, CD-ROM Proceedings of the 3rd International Workshop on Video Processing and Quality Metrics for Consumer Electronics VPQM-07, 2007.
Turkish Journal of Electrical Engineering and Computer Science-Cover
  • ISSN: 1300-0632
  • Yayın Aralığı: Yılda 6 Sayı
  • Yayıncı: TÜBİTAK