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Proceedings Paper

GLCM and neural network-based watermark identification
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Paper Abstract

In this work, we extend our previous research on gray level co-occurrence matrix (GLCM) based watermark embedding in the discrete cosine transform (DCT) domain to the discrete wavelet transform (DWT) domain. The GLCM method incorporated human visual system information into the embedding process making the watermark more transparent. DWT techniques allow for more compression as fewer coefficients are required to reconstruct an image. In addition, DWT methods will not exhibit block artifacts commonly encountered when applying block based DCT methods. The watermark identification is further enhanced using neural networks. In this research, daubechies wavelets are utilized to evaluate the efficiency of the watermark identification while the method is subjected to multiple attacks such as filtering, compression, or rotation. The results are then compared with previously published methods by the authors such as LMS based correlation and adaptive DWT based watermark identification.

Paper Details

Date Published: 3 September 2008
PDF: 10 pages
Proc. SPIE 7075, Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications XI, 70750A (3 September 2008); doi: 10.1117/12.795787
Show Author Affiliations
Lifford McLauchlan, Texas A&M Univ., Kingsville (United States)
Mehrübe Mehrübeoğlu, Texas A&M Univ., Corpus Christi (United States)


Published in SPIE Proceedings Vol. 7075:
Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications XI
Mark S. Schmalz; Gerhard X. Ritter; Junior Barrera; Jaakko T. Astola, Editor(s)

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