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Proceedings Paper

Neural network-based watermark embedding and identification
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Paper Abstract

In previous research, we have shown the ability of neural networks to improve the performance of the watermark system to identify the watermark under different attacks. On the other hand, in this work we apply neural networks to embed the watermark in the discrete wavelet transform (DWT) domain. We then use features based on principal component analysis (PCA) to blindly identify the watermark. PCA reduces the dimensionality as well as the redundancies of the data. Neural networks classifiers are implemented to determine whether the watermark is present. Different features are used to test the performance of the method. The efficacy of the technique is then compared to previous techniques such as the gray level co-occurrence matrix (GLCM) based or the LMS enhanced watermark identification. The comparative results from the previously used methods are presented in this paper.

Paper Details

Date Published: 3 September 2008
PDF: 9 pages
Proc. SPIE 7075, Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications XI, 70750B (3 September 2008); doi: 10.1117/12.795794
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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