Abstract
Perceptual image hashing finds increasing attention in several multimedia security applications such as image identification/authentication, tamper detection, and watermarking. Robust feature extraction is the main challenge in hashing schemes. Local binary pattern (LBP) is a new feature which is due to its simplicity, discriminative power, computational efficiency, and robustness to illumination changes has been used in various image applications. In this paper, we propose a robust image hashing scheme using center-symmetric local binary patterns (CSLBP). In the proposed image hashing, CSLBP features are extracted from each non-overlapping block within the original gray-scale image. For each block, the final hash code is obtained by inner product of its CSLBP feature vector and a pseudorandom weight vector. Furthermore, singular value decomposition (SVD) is combined with CSLBP to introduce a more robust hashing method called SVD-CSLBP. Performances of the proposed hashing schemes are evaluated with two groups of popular applications in perceptual image hashing schemes: image identification and image authentication. Experimental results show that the proposed methods are robust to a wide range of distortions and attacks such as additive noise, blurring, brightness changes and JPEG compression. Moreover, the proposed methods have this capability to localize the tampering area, which is not possible in all hashing schemes.
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References
Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Transa Pattern Anal Mach Intell 28:2037–2041
Birajdar GK, Mankar VH (2013) Digital image forgery detection using passive techniques: a survey. Digit Investig 10:226–245
Ching-Yung L, Shih-Fu C (2001) A robust image authentication method distinguishing JPEG compression from malicious manipulation. IEEE Trans Circuits Syst Video Technol 11:153–168
Corel (2001) test set.[Online]. http://wang.ist.psu.edu/~jwang/test1.tar. Accessed 10 Feb 2013
Cox I, Miller M, Bloom J, Fridrich J, Kalker T (2008) Digital Watermarking and Steganography, Morgan Kaufmann Publishers Inc
Davarzani R, Mozaffari S, Yaghmaie K (2015) Scale- and rotation-invariant texture description with improved local binary pattern features. Signal Processing,111: 274-293
Davarzani R, Yaghmaie K, Mozaffari S, Tapak M (2013) Copy-move forgery detection using multiresolution local binary patterns. Forensic Sci Int 231:61–72
De Roover C, De Vleeschouwer C, Lefebvre F, Macq B (2005) Robust image hashing based on radial variance of pixels, in: Image Processing, 2005. ICIP 2005. IEEE International Conference on, pp. III-77-80
Farid H (2009) A survey of image forgery detection. IEEE Signal Process Mag 2:16–25
Fridrich J, Goljan M (2000) Robust hash functions for digital watermarking, in: Information Technology: Coding and Computing, 2000. Proceedings. International Conference on, pp. 178–183
Guo X, Hatzinakos D (2007) Content Based Image Hashing Via Wavelet and Radon Transform, in: H.S. Ip, O. Au, H. Leung, M.-T. Sun, W.-Y. Ma, S.-M. Hu (Eds.) Advances in Multimedia Information Processing – PCM 2007, Springer Berlin Heidelberg, pp. 755–764
Guo Z, Zhang D (2010) A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Proc 19:1657–1663
Guoying Z, Ahonen T, Matas J, Pietikainen M (2012) Rotation-invariant image and video description with local binary pattern features. IEEE Trans Image Proc 21:1465–1477
Haouzia A, Noumeir R (2008) Methods for image authentication: a survey. Multimed Tools Appl 39:1–46
Heikkila M, Pietikainen M (2006) A texture-based method for modeling the background and detecting moving objects. IEEE Trans Pattern Anal Mach Intell 28:657–662
Heikkilä M, Pietikäinen M, Schmid C (2009) Description of interest regions with local binary patterns. Pattern Recogn 42:425–436
Kozat SS, Venkatesan R, Mihcak MK (2004) Robust perceptual image hashing via matrix invariants, in: Image Processing, 2004. ICIP ’04. 2004 International Conference on, pp. 3443–3446 Vol. 3445
Lefbvre F, Macq B, Legat J D, (2002) RASH: RAdon Soft Hash algorithm
Lei Y, Wang Y, Huang J (2011) Robust image hash in Radon transform domain for authentication. Signal Processing Image Commun 26:280–288
Liu W, Wang Y, Li S (2011) LBP feature extraction for facial expression recognition. J Inf Compu Sci 8:412–421
Monga V (2005) Perceptually based methods for robust image hashing (Ph.D. thesis), in: Electrical and Computer Engineering, Electrical and Computer Engineering, The University of Texas at Austin, Austin (Texas), pp. 120
Monga V, Mihcak MK (2007) Robust and secure image hashing via Non-negative matrix factorizations. IEEE Trans Inf Forensics and Secur 2:376–390
Monga V, Vats D, Evans B.L (2005) Image Authentication Under Geometric Attacks Via Structure Matching, in: Multimedia and Expo, 2005. ICME 2005. IEEE International Conference on, pp. 229–232
Monga V, x, x00E, M.K. ak, Robust and Secure Image Hashing via Non-Negative Matrix Factorizations, Information Forensics and Security, IEEE Transactions on, 2 (2007) 376–390.
Ng T T, Chang S F, Hsu Y F, Pepeljugoski M (2005) Columbia Photographic Images and Photorealistic Computer Graphics Dataset, in: ADVENT Technical Report, #203-2004-3, Columbia University
Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29:51–59
Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24:971–987
Pietikäinen M, Hadid A, Zhao G, Ahonen T (2011) Computer Vision Using Local Binary Patterns, in: Computer Vision Using Local Binary Patterns, Springer London, pp. E1-E2
Qin C, Chang C-C, Tsou P-L (2013) Robust image hashing using non-uniform sampling in discrete fourier domain. Digital Signal Process 23:578–585
Rivest R (1992) The MD5 Message-Digest Algorithm, RFC Editor
Roy S, Sun Q (2007) Robust Hash for Detecting and Localizing Image Tampering, in: Image Processing, 2007. ICIP 2007. IEEE International Conference on, pp. VI - 117-VI - 120.
Shan C, Gong S, McOwan PW (2009) Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis Comput 27:803–816
Shuo-zhong W, Xin-peng Z (2007) Recent development of perceptual image hashing. J of Shanghai Univ 11:323–331
Stamm MC, Wu M, Liu KJR (2013) Information forensics: an overview of the first decade. Access, IEEE 1:167–200
Sun R, Zeng W (2014) Secure and robust image hashing via compressive sensing. Multimed Tools Appl 70:1651–1665
Swaminathan A, Mao Y, Wu M (2006) Robust and secure image hashing. IEEE Trans Inf Forensics Secur 1:215–230
Tang Z, Wang S, Zhang X, Wei W, Zhao Y (2011) Lexicographical framework for image hashing with implementation based on DCT and NMF. Multimed Tools Appl 52:325–345
Venkatesan R, Koon SM, Jakubowski MH, Moulin P (2000) Robust image hashing, in: Image Processing, 2000. Proceedings. 2000 International Conference on, pp. 664–666 vol.663.
Li W (2012) Perceptual Multimedia Hashing (Ph.D. thesis), in: Department of Electrical Engineering (ESAT), Department of Electrical Engineering (ESAT), Katholieke Universiteit Leuven, Heverlee (Belgium) pp. 208
Wu M, Mao Y, Swaminathan A (2007) A Signal Processing and Randomization Perspective of Robust and Secure Image Hashing, in: Statistical Signal Processing, 2007. SSP ’07. IEEE/SP 14th Workshop on, pp. 166–170
Lv X, Wang ZJ (2008) Fast Johnson-Lindenstrauss Transform for robust and secure image hashing, in: Multimedia Signal Processing, 2008 I.E. 10th Workshop on, pp. 725–729
Lv X, Wang ZJ (2012) Perceptual image hashing based on shape contexts and local feature points. IEEE Trans Inf Forensics Secur 7:1081–1093
Li Z, Liu G, Yang Y, You J (2012) Scale- and rotation-invariant local binary pattern using scale-adaptive texton and subuniform-based circular shift. IEEE Trans Image Proc 21:2130–2140
Acknowledgment
The authors are grateful for the anonymous reviewers’ insightful comments and valuable suggestions sincerely. We would like appreciate Dr. Xudong Lv, Dr. Vishal Monga and Dr. Divyanshu Vats for letting us to use their codes for comparing the results.
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Davarzani, R., Mozaffari, S. & Yaghmaie, K. Perceptual image hashing using center-symmetric local binary patterns. Multimed Tools Appl 75, 4639–4667 (2016). https://doi.org/10.1007/s11042-015-2496-6
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DOI: https://doi.org/10.1007/s11042-015-2496-6