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Strintzis, “Robust image watermarking in the subband or discrete cosine transform domain,” in Proc. Eur. Signal Processing Conf. (EUSIPCO), Greece, Sept. 1998, pp. 2285–2288. [21] I. J. Cox, J. Kilian, F. T. Leighton, and T. Shamoon, “Secure spread spectrum perceptual watermarking for images, audio and video,” IEEE Trans. Image Processing, vol. 6, pp. 1673–1687, Dec. 1997. [22] I. J. Cox, M. L. Miller, and A. McKellips, “Watermarking as communications with side information,” Proc. IEEE, vol. 87, pp.

Represent an MB under considAs shown in Fig. 3, let eration and , , , and are its four connected MBs in the north, south, east and west directions respectively. Further, let represent boundary pixels inside the MB , and represent boundary pixels (adjacent to ) in one of the neighboring MBs. Then the AIDB of with an MB , is given as (1) where an MB is considered as of dimension ( here). The AIDB can also be calculated for more than one neighboring block boundaries of MB as (2) is larger than some threshold, MB is considIf ered as being corrupted.

12( a) and (b), the experimental results follow the theoretical ROC curves quite closely. Thus, the theoretically predicted ROC curves and in general the prediction of error probabilities in the presence of quantization can be considered reliable, since they coincide with the actual empirical values. Finally, these results confirm the theoretical analysis of the effects of quantization on improved watermark detection of Section IV. VI. CONCLUSION In this paper, we examined the effects of the very common and nonmalicious quantization attack on watermarking schemes.

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