Abstract
Tampered image discovery from similar images is a challenging problem of multimedia security. Aiming at this issue, we propose a robust image hashing with invariant moments. Specifically, the proposed hashing firstly converts the input image into a normalized image by interpolation, filtering and color space conversion. Then it divides the normalized image into overlapping blocks and extracts invariant moments of blocks to form a feature matrix. Finally, the feature matrix is compressed to make a short hash. Hash similarity is determined by measuring similarity between hash segments with correlation coefficient. Experimental results indicate that our hashing is robust against normal digital operations and can efficiently distinguish tampered images from similar images. Comparisons show that our hashing is better than some notable hashing algorithms in classification performances between robustness and content sensitivity.
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References
Zhu, X., Zhang, L., Huang, Z.: A sparse embedding and least variance encoding approach to hashing. IEEE Transactions on Image Processing 23, 3737–3750 (2014)
Ahmed, F., Siyal, M.Y., Abbas, V.U.: A secure and robust hash-based scheme for image authentication. Signal Processing 90, 1456–1470 (2010)
Winter, C., Steinebach, M., Yannikos, Y.: Fast indexing strategies for robust image hashes. Digital Investigation 11, S27–S35 (2014)
Fridrich, J., Goljan, M.: Robust hash functions for digital watermarking. In: IEEE International Conference on Information Technology: Coding and Computing, pp. 178–183. IEEE Press, New York (2000)
Tang, Z., Wang, S., Zhang, X., Wei, W., Su, S.: Robust image hashing for tamper detection using non-negative matrix factorization. Journal of Ubiquitous Convergence and Technology 2, 18–26 (2008)
Lv, X., Wang, Z.J.: Reduced-reference image quality assessment based on perceptual image hashing. In: IEEE International Conference on Image Processing, pp. 4361–4364. IEEE Press, New York (2009)
Lu, W., Wu, M.: Multimedia forensic hash based on visual words. In: IEEE International Conference on Image Processing, pp. 989–992. IEEE Press, New York (2010)
Zhu, X., Huang, Z., Cheng, H., Cui, J., Shen, H.: Sparse hashing for fast multimedia search. ACM Transactions on Information Systems 31, 9 (2013)
Zhu, X., Huang, Z., Cheng, H., Shen, H., Zhao, X.: Linear cross-modal hashing for efficient multimedia search. In: the 21st ACM International Conference on Multimedia, pp. 143–152. ACM, New York (2013)
Tang, Z., Dai, Y., Zhang, X., Zhang, S.: Perceptual image hashing with histogram of color vector angles. In: Huang, R., Ghorbani, A.A., Pasi, G., Yamaguchi, T., Yen, N.Y., Jin, B. (eds.) AMT 2012. LNCS, vol. 7669, pp. 237–246. Springer, Heidelberg (2012)
Tang, Z., Wang, S., Zhang, X., Wei, W.: Structural feature-based image hashing and similarity metric for tampering detection. Fundamenta Informaticae 106, 75–91 (2011)
Venkatesan, R., Koon, S.-M., Jakubowski, M.H., Moulin, P.: Robust image hashing.. In: IEEE International Conference on Image Processing, pp. 664–666. IEEE Press, New York (2000)
Lin, C.Y., Chang, S.F.: A robust image authentication system distinguishing JPEG compression from malicious manipulation. IEEE Transactions on Circuits System and Video Technology 11, 153–168 (2001)
Swaminathan, A., Mao, Y., Wu, M.: Robust and secure image hashing. IEEE Transactions on Information Forensics and Security 1, 215–230 (2006)
Monga, V., Mihcak, M.K.: Robust and secure image hashing via non-negative matrix factorizations. IEEE Transactions on Information Forensics and Security 2, 376–390 (2007)
Ou, Y., Rhee, K.H.: A key-dependent secure image hashing scheme by using Radon transform. In: IEEE International Symposium on Intelligent Signal Processing and Communication Systems, pp. 595–598. IEEE Press, New York (2009)
Kang, L., Lu, C., Hsu, C.: Compressive sensing-based image hashing. In: IEEE International Conference on Image Processing, pp. 1285–1288. IEEE Press, New York (2009)
Li, Y., Lu, Z., Zhu, C., Niu, X.: Robust image hashing based on random Gabor filtering and dithered lattice vector quantization. IEEE Transactions on Image Processing 21, 1963–1980 (2012)
Tang, Z., Dai, Y., Zhang, X.: Perceptual hashing for color images using invariant moments. Applied Mathematics & Information Sciences 6, 643S–650S (2012)
Tang, Z., Dai, Y., Zhang, X., Huang, L., Yang, F.: Robust image hashing via colour vector angles and discrete wavelet transform. IET Image Processing 8, 142–149 (2014)
Hu, M.K.: Visual pattern recognition by moment invariants. IRE Transaction on Information Theory 8, 179–187 (1962)
Hsia, T.C.: A note on invariant moments in image processing. IEEE Transactions on Systems, Man, and Cybernetics 11, 831–834 (1981)
Goshtasby, A.: Template matching in rotated images. IEEE Transactions on Pattern Analysis and Machine Intelligence 7, 338–344 (1985)
Tang, Z., Zhang, X., Dai, X., Yang, J., Wu, T.: Robust image hash function using local color features. AEÜ-International Journal of Electronics and Communications 67, 717–722 (2013)
Petitcolas, F.A.P.: Watermarking schemes evaluation. IEEE Signal Processing Magazine 17, 58–64 (2000)
Ground Truth Database, http://www.cs.washington.edu/reseach/imagedatabase/groundtruth/
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Tang, Z., Yu, J., Zhang, X., Zhang, S. (2014). Discovery of Tampered Image with Robust Hashing. In: Luo, X., Yu, J.X., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2014. Lecture Notes in Computer Science(), vol 8933. Springer, Cham. https://doi.org/10.1007/978-3-319-14717-8_9
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DOI: https://doi.org/10.1007/978-3-319-14717-8_9
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