GLCM and neural network-based watermark identification

Lifford McLauchlan; Mehrübe Mehrübeoğlu

doi:10.1117/12.795787

3 September 2008 GLCM and neural network-based watermark identification

Proceedings Volume 7075, Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications XI; 70750A (2008) https://doi.org/10.1117/12.795787
Event: Optical Engineering + Applications, 2008, San Diego, California, United States

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.

Citation Download Citation

Lifford McLauchlan and Mehrübe Mehrübeoğlu "GLCM and neural network-based watermark identification", Proc. SPIE 7075, Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications XI, 70750A (3 September 2008); https://doi.org/10.1117/12.795787

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