Steganography and watermarking of digital images using singular value decomposition

Abstract Singular Value decomposition (SVD), a scheme for matrix diagonalization, has shown promising results in the area of digital image compression. An image matrix is approximated with a rank lower than that of the original image without affecting its perceptual quality. This paper addresses two important aspects of information hiding using SVD namely steganography and watermarking. The lesser significant singular values are used to encode secret messages and carry out hidden communication. Watermarking schemes are designed by slightly modifying the most significant singular values for encoding vital data in the media. By manipulating singular values in different ways, we show the possibility of high-capacity steganography as well as robust watermarking schemes with the ability to resist attacks by active wardens monitoring the communication channels.

[1]  Tieniu Tan,et al.  An SVD-based watermarking scheme for protecting rightful ownership , 2002, IEEE Trans. Multim..

[2]  D. Kalman.  A Singularly Valuable Decomposition: The SVD of a Matrix , 1996 .

[3]  Walter Bender,et al.  Techniques for Data Hiding , 1996, IBM Syst. J..

[4]  Stephan Katzenbeisser,et al.  Information Hiding Techniques for Steganography and Digital Watermaking , 1999 .

[5]  Kuo-Liang Chung,et al.  A novel SVD- and VQ-based image hiding scheme , 2001, Pattern Recognit. Lett..

[6]  Markus G. Kuhn,et al.  Attacks on Copyright Marking Systems , 1998, Information Hiding.

[7]  Martin F. H. Schuurmans,et al.  Digital watermarking , 2002, Proceedings of ASP-DAC/VLSI Design 2002. 7th Asia and South Pacific Design Automation Conference and 15h International Conference on VLSI Design.