Abstract
This paper addresses feature extraction techniques for robust watermarking. Geometric distortion attacks desynchronize the location of the inserted watermark and hence prevent watermark detection. Watermark synchronization, which is a process of finding the location for watermark insertion and detection, is crucial to design robust watermarking. One solution is to use image features. This paper reviews feature extraction techniques that have been used in featurebased watermarking: the Harris corner detector and the Mexican Hat wavelet scale interaction method. We also evaluate the scale-invariant keypoint extractor in comparison with other techniques in aspect of watermarking. After feature extraction, the set of triangles is generated by Delaunay tessellation. These triangles are the location for watermark insertion and detection. Redetection ratio of triangles is evaluated against geometric distortion attacks as well as signal processing attacks. Experimental results show that the scale-invariant keypoint extractor is appropriate for robust watermarking.
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References
Kutter, M.: Watermarking resisting to translation, rotation and scaling. In: Proc. of SPIE, vol. 3528, pp. 423–431 (1998)
Pereira, S., Pun, T.: Robust template matching for affine resistant image watermark. IEEE Trans. on Image Processing 9, 1123–1129 (2000)
Ruanaidh, J.J.K.O., Pun, T.: Rotation, scale and translation invariant spread spectrum digital image watermarking. Signal Processing 66, 303–317 (1998)
Lin, C., Cox, I.J.: Rotation, scale and translation resilient watermarking for images. IEEE Trans. on Image Processing 10, 767–782 (2001)
Simitopoulos, D., Koutsonanos, D.E., Strintzis, M.G.: Robust image watermarking based on generalized radon transformation. IEEE Trans. on Circuits and Systems for Video Technology 13, 732–745 (2003)
Arghoniemy, M., Twefik, A.H.: Geometric invariance in image watermarking. IEEE Trans. on Image Processing 13, 145–153 (2004)
Kutter, M., Bhattacharjee, S.K., Ebrahimi, T.: Towards second generation watermarking schemes. Proc. of ICIP, vol. 1, pp. 320–323 (1999)
Bas, P., Chassery, J.-M., Macq, B.: Geometrically invariant watermarking using feature points. IEEE Trans. on Image Processing 11, 1014–1028 (2002)
Nikolaidis, A., Pitas, I.: Region-based image watermarking. IEEE Trans. on Image Processing 10, 1726–1740 (2001)
Tang, C.W., Hang, H.-M.: A feature-based robust digital image watermarking scheme. IEEE Trans. on Signal Processing 51, 950–959 (2003)
Manjunath, B.S., Shekhar, C., Chellappa, R.: A new approach to image feature detection with applications. Pattern Recognition 4, 627–640 (1996)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. International Journal of Computer Vision 60, 63–86 (2004)
Tuytelaars, T., Gool, L.V.: Matching widely separated views based on affine invariant regions. International Journal of Computer Vision 59, 61–85 (2004)
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© 2005 Springer-Verlag Berlin Heidelberg
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Lee, HY., Kang, I.K., Lee, HK., Suh, YH. (2005). Evaluation of Feature Extraction Techniques for Robust Watermarking. In: Barni, M., Cox, I., Kalker, T., Kim, HJ. (eds) Digital Watermarking. IWDW 2005. Lecture Notes in Computer Science, vol 3710. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551492_32
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DOI: https://doi.org/10.1007/11551492_32
Publisher Name: Springer, Berlin, Heidelberg
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