Abstract
In this paper, we present a novel image digital watermarking technique based on Kernel Independent Component Analysis (KICA). Use the nice characteristic of the KICA, which can results the blind separation of nonlinearly mixed signals, the imperceptibility and robustness requirements of watermarks are fulfilled and optimized. In the proposed scheme, the watermark image is first transformed by Arnold method, and then embedded into the lowest frequency subband in DWT domain. The recovery of owner’s image is turning the watermarked image into DWT domains then use KICA to extract the watermark. Finally the watermark is transformed by Arnold method again, so we can get the original watermark image. Experimental results show that the proposed method gains better performance in robustness than that of ICA with respect to traditional image processing including cropping, filtering, add noise and JPEG image compression.
Supported by the Doctor Degree Teacher Research Fund in North China Electric Power University.
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References
Kundur, D., Hatzinakos, D.: Digital Watermarking Using Multiresolution Wavelet Decomposition. In: Proc. ICAssp, pp. 2969–2972 (1998)
Cox, J., et al.: Secure Spread Spectrum Watermarking for Multimedia. IEEE Trans. Image Processing 6, 1673–1687 (1997)
Barni, M., Bartolini, F., Cappellini, V.: A DCT-domian System for Robust Image Watermarking. Signal Processing 66, 357–372 (1998)
Gonzalez-Serrano, F.J., Molina-Bulla, H.Y., Murillo-Fuente, J.J.: Independent Component Analysis Applied to Digital Image Watermarking. In: Proc. IEEE, Int. Conference on Acoustics, Speech, and Blind Signal Separation (ICASSP’01), vol. 3, pp. 1997–2000 (2001)
Liu, J., Zhang, X., Sun, J.: A New Image Watermarking Scheme Based on DWT and ICA. In: IEEE International Conference on Neural Networks & Signal processing (ICNNSP’03), pp. 1489–1492 (2003)
Bach, F.R.: Kernel Independent Component Analysis. Journal of Machine Learning Research 3, 1–48 (2002)
Zhang, Y.-K., Liu, C.-q.: Face Recognition Based on Kernel Independent Components Analysis. Optical Technique 30(5), 613–622 (2004)
Scholkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)
Cheng, J., Liu, Q., Lu, H.: Texture Classification Using Kernel Independent Component Analysis. In: ICPR, vol. 17, pp. 1051–4651 (2004)
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© 2007 Springer-Verlag Berlin Heidelberg
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Li, Y., Wu, K., Ma, Y., Zhang, S. (2007). Image Digital Watermarking Technique Based on Kernel Independent Component Analysis. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2007. Lecture Notes in Computer Science(), vol 4482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72530-5_56
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DOI: https://doi.org/10.1007/978-3-540-72530-5_56
Publisher Name: Springer, Berlin, Heidelberg
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