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Splitting Histogram Equalization based Secure Watermark Detection
Abstract
Compressive Sensing (CS) unifies sampling and compression in order to reduce the data acquisition and computational load at sensors, at the cost of increased computation at the intended receiver. It is identified that a secret key is used in the CS algorithm for embedding and detecting the watermark and extracting the information. It is then proposed by using splitting histogram equalization method to get a watermarked image and then implemented in Field Programming Gate Array (FPGA).
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References
T. Bianchi and A. Piva, “Secure watermarking for multimedia content protection: A review of its benefits and open issues,” IEEE Signal process. Mag.,vol. 30,no.2,pp. 87-96, Mar. 2013.
Z. Erkin. A. Piva, S. Katzenbeisser, R. Lagendijk, J. Shokrollhi, G. Neven, et al., “Protection and retrieval of encrypted multimedia content: When cryptography meets signal processing,” EURASIP J.Inf.Security, vol. 7. No. 2.pp. 1-20, 2007.
J. Eggers, J. Su, and B. Girod,”public key watermarking by eigenvectors of linear transforms,”in Proc. Euro. Signal Process. Conf., 2000
S. Craver and S. Katzenbeisser, ”Security analysis of public –key watermarking schemes,” in Proc. Math. Data/Image Coding, Compress., Encryption IV, Appl., vol. 4475. 2001, pp. 172-182.
A. Adelsbach and A. Sadeghi, “Zero-knowledge watermark detection and proof of ownership,” in Proc. 4th Int. Workshop Inf. Hiding, vol. 2137. 2001, pp. 273-288.
J. R. Troncoso-Pastoriza and F.Perez-Gonzales, “Zero-knowledge watermark detector robust to sensitivity attacks,” in Proc. ACM Multimedia Security Workshop, 2006, pp. 97-107.
M. Malkin and T. Kalker. “A cryptographic method for secure watermark detection,” in Proc. 8th Int. Workshop Inf. Hiding. 2006, pp. 26-41.
W. Zeng and B. Liu, “A statistical watermark detection technique without using original images for resolving rightful ownerships of digital images,” IEEE Trans. Image Process., vol. 8, no. 11, pp. 1534-1548, Nov. 1999.
N. A. Weiss, A Course in Probability. Reading, MA, USA: Addison-Wesley,2005, pp. 385-386.
O. Goldreich, The Foundations of Cryptography. Cambridge, U.K.: Cambridge Univ. Press, 2004.
K. Liu. H. Kargupta and J. Ryan, “Random projection-based multiplicative data perturbation for privacy preserving distributed data mining.” IEEE Trans. Knowl. Data Eng., vol. 18, no. 1,pp. 92-106. Jan. 2006.
W. Lu, A. L. Varna, A. Swaminathan, and M. Wu, “Secure image retrieval through feature protection,” in Proc. IEEE Conf. Acoust., Speech Signal Process., Apr. 2009, pp. 1533–1536.
W. Lu, A. L. Varna, and M.Wu, “Security analysis for privacy preserving search for multimedia,” in Proc. IEEE 17th Int. Conf. Image Process., Sep. 2010, pp. 2093–2096.
D. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289–1306, Apr. 2006.
M. Rudelson and R. Vershynin, “Sparse reconstructions by convex relaxation: Fourier and Gaussian measurements,” in Proc. Conf. Inf. Sci. Syst., Mar. 2006, pp. 207–212.
J. Tropp and A. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Trans. Inf. Theory, vol. 53, no. 12, pp. 4655–4666, Dec. 2007.
M. Davenport, P. Boufounos, M. Wakin, and R. Baraniuk, “Signal processing with compressive measurements,” IEEE J. Sel. Topics Signal Process., vol. 4, no. 2, pp. 445–460, Apr. 2010.
R. Calderbank, S. Jafarpour, and R. Schapire. (2009).Compressed learning: Universal sparse dimensionality deduction and learning in the measurement domain [Online].
D. Hsu, S. M. Kakade, J. Langford, and T. Zhang, “Multi-label prediction via compressed sensing,” in Proc. NIPS, 2009, pp. 772-780.
Y. Rachlin and D. Baron, “The secrecy of compressed sensing measurement,” in Proc. 46th Annu. Allerton Conf. Commun., Control, Comput., 2008, pp. 813-817.
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