DocumentCode :
1093728
Title :
Design and statistical analysis of a hash-aided image watermarking system
Author :
Cannons, Jillian ; Moulin, Pierre
Author_Institution :
Electr. & Comput. Eng. Dept., Univ. of Illinois, Urbana, IL, USA
Volume :
13
Issue :
10
fYear :
2004
Firstpage :
1393
Lastpage :
1408
Abstract :
This paper develops a joint hashing/watermarking scheme in which a short hash of the host signal is available to a detector. Potential applications include content tracking on public networks and forensic identification. The host data into which the watermark is embedded are selected from a secret subset of the full-frame discrete cosine transform of an image, and the watermark is inserted through multiplicative embedding. The hash is a binary version of selected original image coefficients. We propose a maximum likelihood watermark detector based on a statistical image model. The availability of a hash as side information to the detector modifies the posterior distribution of the marked coefficients. We derive Chernoff bounds on the receiver operating characteristic performance of the detector. We show that host-signal interference can be rejected if the hash function is suitably designed. The relative difficulty of an eavesdropper\´s detection problem is also determined; the eavesdropper does not know the secret key used. Monte Carlo simulations are performed using photographic test images. Finally, various attacks on the watermarked image are introduced to study the robustness of the derived detectors. The joint hashing/watermarking scheme outperforms the traditional "hashless" watermarking technique.
Keywords :
Monte Carlo methods; discrete cosine transforms; image processing; interference (signal); maximum likelihood detection; statistical analysis; watermarking; Chernoff bounds; eavesdropper detection problem; forensic identification; full-frame discrete cosine transform; hash-aided image watermarking system; host-signal interference; maximum likelihood watermark detector; multiplicative embedding; statistical analysis; statistical image model; Availability; Detectors; Discrete cosine transforms; Forensics; Interference; Maximum likelihood detection; Performance evaluation; Statistical analysis; Testing; Watermarking; Algorithms; Computer Security; Computer Simulation; Data Compression; Hypermedia; Image Interpretation, Computer-Assisted; Models, Statistical; Patents as Topic; Pattern Recognition, Automated; Product Labeling; Reproducibility of Results; Sensitivity and Specificity;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/TIP.2004.834660
Filename :
1331450
Link To Document :
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