DocumentCode
961799
Title
Optimal Signature Design for Spread-Spectrum Steganography
Author
Gkizeli, Maria ; Pados, Dimitris A. ; Medley, Michael J.
Author_Institution
Dept. of Electr. Eng., State Univ. of New York, Buffalo, NY
Volume
16
Issue
2
fYear
2007
Firstpage
391
Lastpage
405
Abstract
For any given host image or group of host images and any (block) transform domain of interest, we find the signature vector that when used for spread-spectrum (SS) message embedding maximizes the signal-to-interference-plus-noise ratio (SINR) at the output of the corresponding maximum-SINR linear filter. We establish that, under a (colored) Gaussian assumption on the transform domain host data, the same derived signature minimizes host distortion for any target message recovery error rate and maximizes the Shannon capacity of the covert steganographic link. Then, we derive jointly optimal signature and linear processor designs for SS embedding in linearly modified transform domain host data and demonstrate orders of magnitude improvement over current SS steganographic practices. Optimized multisignature/multimessage embedding in the same host data is studied as well
Keywords
Gaussian processes; cryptography; data encapsulation; digital signatures; filtering theory; image coding; transforms; Gaussian assumption; SINR; Shannon capacity; host images; linear filter; message recovery error rate; multimessage embedding; multisignature embedding; optimal signature design; signal-to-interference-plus-noise ratio; spread-spectrum steganography; transform domain; Discrete Fourier transforms; Discrete cosine transforms; Discrete wavelet transforms; Fourier transforms; Nonlinear filters; Signal processing; Signal to noise ratio; Spread spectrum communication; Steganography; Watermarking; Authentication; covert communications; data hiding; distortion; linear filters; signal-to-interference-plus-noise ratio (SINR); spread spectrum; steganography; watermarking; Algorithms; Computer Graphics; Computer Security; Data Compression; Image Enhancement; Image Interpretation, Computer-Assisted; Numerical Analysis, Computer-Assisted; Patents as Topic; Product Labeling; Signal Processing, Computer-Assisted;
fLanguage
English
Journal_Title
Image Processing, IEEE Transactions on
Publisher
ieee
ISSN
1057-7149
Type
jour
DOI
10.1109/TIP.2006.888345
Filename
4060938
Link To Document