• 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