Unitary embedding for data hiding with the SVD

Steganography is the study of data hiding for the purpose of covert communication. A secret message is inserted into a cover file so that the very existence of the message is not apparent. Most current steganography algorithms insert data in the spatial or transform domains; common transforms include the discrete cosine transform, the discrete Fourier transform, and discrete wavelet transform. In this paper, we present a data-hiding algorithm that exploits a decomposition representation of the data instead of a frequency-based transformation of the data. The decomposition transform used is the singular value decomposition (SVD). The SVD of a matrix A is a decomposition A = USV T in which S is a nonnegative diagonal matrix and U and V are orthogonal matrices. We show how to use the orthogonal matrices in the SVD as a vessel in which to embed information. Several challenges were presented in order to accomplish this, and we give effective solutions to these problems. Preliminary results show that information-hiding using the SVD can be just as effective as using transform-based techniques. Furthermore, different problems arise when using the SVD than using a transform-based technique. We have applied the SVD to image data, but the technique can be formulated for other data types such as audio and video.