Abstract
With the wide use of sophisticated photo editing tools, digital image manipulation becomes very convenient, which makes the detection of image tampering significant. Image sharpening, which aims to enhance the contrast of edges in an image, is a ubiquitous image tampering operation. The detection of image sharpening can serve as a reliable clue for image forgery. In this paper, we propose a novel image sharpening detection method based on multiresolution overshoot artifact analysis (MOAA). By building the relationship between the overshoot artifact strength and the slope of a sharpened edge, we find that although undergoing the same sharpening operation, the edge with large slope will present a stronger overshoot artifact than the one with small slope. Based on this finding, we use the nonsubsampled contourlet transform (NSCT) to classify the image edge points into three categories, i.e., weak, middle and strong edge points and measure the overshoot artifact of each category respectively. A cascaded decision strategy is adopted to decide an image is sharpened or not. Experimental results on digital images with various sharpening operators demonstrate the superiority of our proposed method when compared with state-of-the-art approaches.
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Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898–916
Bahrami K, Kot AC, Li L, Li H (2015) Blurred image splicing localization by exposing blur type inconsistency. IEEE Trans Inf Forensic Secur 10(5):999–1009
Bas P, Furon T (2007) Break our watermarking system [online]. Available: http://bows2.gipsa-lab.inpg.fr
Bianchi T, Piva A (2012) Detection of non-aligned double jpeg compression based on integer periodicity maps. IEEE Trans Inf Forensic Secur 7(2):842–848
Birajdar GK, Mankar VH (2013) Digital image forgery detection using passive techniques: a survey. Digit Investig 10(3):226–245
Cao G, Zhao Y, Ni R (2009) Detection of image sharpening based on histogram aberration and ringing artifacts. In: Proc. IEEE International Conference on Multimedia and Expo, pp. 1026–1029
Cao G, Zhao Y, Ni R, Kot AC (2011) Unsharp masking sharpening detection via overshoot artifacts analysis. IEEE Signal Processing Letters 18(10):603–606
Cao G, Zhao Y, Ni R, Li X (2014) Contrast enhancement-based forensics in digital images. IEEE Trans Inf Forensic Secur 9(3):515–525
Chen M, Fridrich J, Goljan M, Lukas J (2008) Determining image origin and integrity using sensor noise. IEEE Trans Inf Forensic Secur 3(1):74–90
Choi CH, Lee HY, Lee HK (2013) Estimation of color modification in digital images by CFA pattern change. Forensic Sci Int 226(1):94–105
Da Cunha AL, Zhou J, Do MN (2006) The nonsubsampled contourlet transform: theory, design, and applications. IEEE Trans Image Process 15(10):3089–3101
Deng C, Gao X, Li X, Tao D (2010) Local histogram based geometric invariant image watermarking. Signal Process 90(12):3256–3264
Ding F, Zhu G, Shi YQ (2013) A novel method for detecting image sharpening based on local binary pattern. In: Proc. International Workshop on Digital-Forensics and Watermarking, pp. 180–191
Fan S, Wang R, Zhang Y, Guo K (2012) Classifying computer generated graphics and natural imaged based on image contour information. Int J Inf Comput Sci 9(10):2877–2895
Ferrara P, Bianchi T, De Rosa A, Piva A (2012) Image forgery localization via fine-grained analysis of CFA artifacts. IEEE Trans Inf Forensic Secur 7(5):1566–1577
Gao X, Deng C, Li X, Tao D (2010) Geometric distortion insensitive image watermarking in affine covariant regions. IEEE Trans Systems, Man, and Cybernetics, Part C 40(3):278–286
Gao L, Song J, Nie F, Zou F, Sebe N, Shen HT (2016). Graph-without-cut: An Ideal Graph Learning for Image Segmentation. In: Proc. AAAI Conference on Artificial Intelligence, pp. 1188–1194
Gloe T, Borowka K, Winkler A (2010) Efficient estimation and large-scale evaluation of lateral chromatic aberration for digital image forensics. In: Proc. SPIE Conference on Media Forensics and Security, p. 754107
Hou X, Zhang T, Xiong G, Zhang Y, Ping X (2014) Image resampling detection based on texture classification. Multimed Tools Appl 72(2):1681–1708
Hsu YF, Chang SF (2010) Camera response functions for image forensics: an automatic algorithm for splicing detection. IEEE Trans Inf Forensic Secur 5(4):816–825
Johnson MK, Farid H (2007) Exposing digital forgeries in complex lighting environments. IEEE Trans Inf Forensic Secur 2(3):450–461
Kang X, Li Y, Qu Z, Huang J (2012) Enhancing source camera identification performance with a camera reference phase sensor pattern noise. IEEE Trans Inf Forensic Secur 7(2):393–402
Liu Q, Cao X, Deng C, Guo X (2011) Identifying image composite through shadow matte consistency. IEEE Trans Inf Forensic Secur 6(3):1111–1122
Liu G, Wang J, Lian S, Dai Y (2013) Detect image splicing with artificial blurred boundary. Math Comput Model 57(11):2647–2659
Lu L, Yang G, Xia M (2013) Anti-forensics for unsharp masking sharpening in digital image. Int J Digital Crime Forensics 5(3):53–65
Mahdian B, Saic S (2008) Blind authentication using periodic properties of interpolation. IEEE Trans Inf Forensic Secur 3(3):529–538
Muammar H, Dragotti PL (2013) An investigation into aliasing in images recaptured from an LCD monitor using a digital camera. In: Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2242–2246
Natural resource conservation service photo gallery [online]. Available: http://photogallery.nrcs.usda.gov/res/sites/PhotoGallery/index.html.
Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987
Redi JA, Taktak W, Dugelay JL (2011) Digital image forensics: a booklet for beginners. Multimed Tools Appl 51(1):133–162
Schaefer G, Stich M (2004) UCID: an uncompressed color image database. In: Proc. SPIE, Storage and Retrieval Methods and Applications for Multimedia, pp. 472–480
Shen Z, Ni J, Chen C (2016) Blind detection of median filtering using linear and nonlinear descriptors. Multimed Tools Appl 75(4):2327–2346
Stamm MC, Liu KJR (2010) Forensic detection of image manipulation using statistical intrinsic fingerprints. IEEE Trans Inf Forensic Secur 5(3):492–506
Thongkamwitoon T, Muammar H, Dragotti PL (2015) An image recapture detection algorithm based on learning dictionaries of edge profiles. IEEE Trans Inf Forensic Secur 10(5):953–968
Wang X, Liu Y, Xu B, Li L, Xue J (2014) A statistical feature based approach to distinguish PRCG from photographs. Comput Vis Image Underst 128:84–93
Zhang R, Wang RD (2015) In-camera jpeg compression detection for doubly compressed images. Multimed Tools Appl 74(15):5557–5575
Acknowledgements
The authors would like to thank the Editor-in-Chief, the handling associate editor and all anonymous reviewers for their considerations and suggestions. This work was supported by the National High Technology Research and Development Program of China (2013AA01A602), the National Natural Science Foundation of China (Grant Nos. 61432014 and 61572388) and Program for Changjiang Scholars and Innovative Research Team in University (No. IRT13088).
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Zhu, N., Deng, C. & Gao, X. Image sharpening detection based on multiresolution overshoot artifact analysis. Multimed Tools Appl 76, 16563–16580 (2017). https://doi.org/10.1007/s11042-016-3938-5
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DOI: https://doi.org/10.1007/s11042-016-3938-5