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Abstrakty
Structural image features are exploited to construct perceptual image hashes in this work. The image is first preprocessed and divided into overlapped blocks. Correlation between each image block and a reference pattern is calculated. The intermediate hash is obtained from the correlation coefficients. These coefficients are finally mapped to the interval [0, 100], and scrambled to generate the hash sequence. A key component of the hashingmethod is a specially defined similarity metric to measure the "distance" between hashes. This similarity metric is sensitive to visually unacceptable alterations in small regions of the image, enabling the detection of small area tampering in the image. The hash is robust against content-preserving processing such as JPEG compression, moderate noise contamination, watermark embedding, re-scaling, brightness and contrast adjustment, and low-pass filtering. It has very low collision probability. Experiments are conducted to show performance of the proposed method.
Wydawca
Czasopismo
Rocznik
Tom
Strony
75--91
Opis fizyczny
Bibliogr. 19 poz., fot., tab., wykr.
Twórcy
autor
autor
autor
autor
- School of Communication and Information Engineering, Shanghai University, 149 Yanchang Road, Shanghai 200072, China., tangzj230@163.com
Bibliografia
- [1] Zhang X., andWang S.:Fragile watermarking with error-free restoration capability,IEEE Transactions on Multimedia, 2008, 10, (8), pp.1490-1499.
- [2] Venkatesan R., Koon S.-M., Jakubowski M. H., and Moulin P.:Robust image hashing, Proc. of IEEE International Conference on Image Processing, Vancouver, BC, Canada, September 2000, pp.664-666.
- [3] Meixner A., and Uhl A.:Analysis of a wavelet-based robust hash algorithm, Proc. SPIE-IS&T - Security, Steganography, and Watermarking of Multimedia Contents VI, San Jose, CA, January 2004, pp.772-783.
- [4] Fridrich J., and Goljan M.:Robust hash functions for digital watermarking, Proc. of IEEE International Conference on Information Technology: Coding and Computing, Las Vergas, USA, March 2000, pp.178-183.
- [5] Lin C. Y., and Chang S. F.:A robust image authentication system distinguishing JPEG compression from malicious manipulation, IEEE Transactions on Circuits System and Video Technology, 2001, 11, (2), pp.153-168.
- [6] Lefebvre F.,Macq B., and Legat J.-D.:RASH: Radon soft hash algorithm, Proc. of European Signal Processing Conference, Toulouse, France, September 2002, pp.299-302.
- [7] Lefebvre F., Czyz J., and Macq B.:A robust soft hash algorithm for digital image signature, Proc. of IEEE International Conference on Image Processing, September 2003, pp.495-498.
- [8] Kozat S. S., Mihcak K., and Venkatesan R.:Robust perceptual image hashing via matrix invariants, Proc. Of IEEE International Conference on Image Processing, Singapore, October 2004, pp.3443-3446.
- [9] Roover C. D., Vleeschouwer C. D., Lefebvre F., andMacq B.:Robust video hashing based on radial projections of key frames, IEEE Transactions on Signal Processing, 2005, 53, (10), pp.4020-4036.
- [10] Swaminathan A., Mao Y., and Wu M.:Robust and secure image hashing, IEEE Transactions on Information Forensics and Security, 2006, 1, (2), pp.215-230.
- [11] Wang S., and Zhang X.:Attacks on perceptual image hashing, Proc. of the 2nd International Conference on Ubiquitous Information Technologies and Applications, Bali, Indonesia, Dec. 2007, pp.199-203.
- [12] Mao Y. and Wu M.:Unicity distance of robust image hashing, IEEE Transactions on Information Forensics and Security, 2007, 2, (3), pp.462-467.
- [13] Li Q., and Roy S.:On the security of non-forgeable robust hash functions, Proc. of IEEE International Conference on Image Processing, San Diego, California, USA, October 2008, pp.3124-3127.
- [14] Monga V., and Mihcak M. K.:Robust and secure image hashing via non-negativematrix factorizations, IEEE Transactions on Information Forensics and Security, 2007, 2, (3), pp.376-390.
- [15] Wang Z., Bovik A. C., Sheikh H. R., and Simoncelli E. P.:Image quality assessment: from error visibility to structural similarity, IEEE Transactions on Image Processing, 2004, 13, (4), pp.600-612.
- [16] Tang Z., Wang S., Zhang X., and Wei W.:Perceptual similarity metric resilient to rotation for application in robust image hashing, Proc. of the 3rd International Conference on Multimedia and Ubiquitous Engineering, Qingdao, China, June 4-6, 2009, pp.183-188.
- [17] Petitcolas F. A. P.:Watermarking schemes evaluation, IEEE Signal Processing Magazine, 2000, 17, (5), pp.58-64.
- [18] Ground Truth Database.[online]. Available: http://www.cs.washington.edu/research/imagedatabase/groundtruth/, accessed May 2008.
- [19] Lehmann E. L., and Romano J. P.:Testing Statistical Hypotheses. (New York, USA, Springer, 2005, 3rd Ed), pp.590-599.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS8-0011-0058