Abstract
Over the past years, digital images have been widely used in the Internet and other applications. Whilst image processing techniques are developing at a rapid speed, tampering with digital images without leaving any obvious traces becomes easier and easier. This may give rise to some problems such as image authentication. A new passive technology for image forensics has evolved quickly during the last few years. Unlike the signature-based or watermark-based methods, the new technology does not need any signature generated or watermark embedded in advance. It assumes that different imaging devices or processing would introduce different inherent patterns into the output images. These underlying patterns are consistent in the original untampered images and would be altered after some kind of manipulations. Thus, they can be used as evidence for image source identification and alteration detection. In this paper, we will discuss this new forensics technology and give an overview of the prior literatures. Some concluding remarks are made about the state of the art and the challenges in this novel technology.
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References
Farid H. Digital doctoring: How to tell the real from the fake, Significance, 2006, 3(4):162–166
Light K, Fonda, Kerry, Photo Fakery. The Washington Post, Saturday 28 Feb. 2004
Voice of the Mirror. Sorry. we were hoaxed: Iraqi PoW abuse pictures handed to us WERE fake. Daily Mirror Newspaper, 15 May 2004
Zhu B, Swanson M, Tewfik A. When seeing isn’t believing [multimedia authentication technologies]. IEEE Signal Processing Magazine, Mar. 2004, 21(2): 40–49
Hartung F, Kutter M. Multimedia watermarking techniques. Proc. IEEE, July 1999, 87(7): 1079–1107
Ng T T, Chang S F, Lin C Y, et al. Multimedia Security Technologies for Digital Rights, chap. Passive-Blind image forensic, Elsvier, 2006
Farid H. Digital image ballistics from jpeg quantization. Tech. Rep. TR2006-583, Department of Computer Science, Dartmouth College, 2006
Adams J, Parulski K, Spaulding K. Color processing in digital cameras. IEEE Micro, Nov.–Dec. 1998, 18(6): 20–30
Kharrazi M, Sencar H, Memon N. Blind source camera identification. In: Proceedings of ICIP’04, 24–27 Oct. 2004, 1: 709–712
Chang C, Lin C. LIBSVM: A library for support vector machines. Software available at:http://www.csie.ntu.edu.tw/:_cjlin/libsvm, 2001
Geradts Z J, Bijhold J, Kieft M, et al. Methods for identification of images acquired with digital cameras. In: SPIE Enabling Technologies for Law Enforcement and Security, 2001, 4232(1): 505–512
Lukas J, Fridrich J, Goljan M. Digital camera identification from sensor pattern noise. IEEE Trans. Information Forensics and Security, June 2006, 1(2): 205–214
Lukas J, Fridrich J, Goljan M. Determining digital image origin using sensor imperfections. In: Proceedings of SPIE Electronic Imaging, Image and Video Communications and Processing 2005, 2005, 5685(1): 249–260
Lukas J, Fridrich J, Goljan M. Digital “bullet scratches” for images. In: Processings of ICIP’ 05, 11–14 Sept. 2005, 3: 65–68
Lukas J, Fridrich J, Goljan M. Detecting digital image forgeries using sensor pattern noise. In: Processings of SPIE Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents, 16–20 Jan. 2006, 6072(1): 60720Y
Swaminathan A, Wu M, Liu K J R. Non-intrusive forensic analysis of visual sensors using output images. In: Proceedings of ICASSP’06, Toulouse, France, 14–19 May 2006, 5: 401–404
Popescu A, Farid H. Exposing digital forgeries in color filter array interpolated images. IEEE Trans. Signal Processing, 2005, 53(10): 3948–3959
Holst G C. CCD Arrays, Cameras, and Displays. 2nd ed. JCD Publishing & SPIE Pres, USA, 1998
Kurosawa K, Kuroki K, Saitoh N. Ccd fingerprint method-identification of a video camera from videotaped images. In: Processings of ICIP’99, 24–28 Oct. 1999, 3: 537–540
Adams J J E. Interactions between color plane interpolation and other image processing functions in electronic photography. In: C.N. Anagnostopoulos, M.P. Lesser, eds., Proceedings of SPIE Electronic Imaging, Cameras and Systems for Electronic Photography and Scientific Imaging, 1995, 2416(1): 144–151
Bayram S, Sencar H, Memon N, et al. Source camera identification based on cfa interpolation. In: Proceedings of ICIP’05, 11–14 Sept. 2005, 3: 69–72
Swaminathan A, Wu M, Liu K J R. Component forensics of digital cameras: A non-intrusive approach. In: Proceedings of Conference on Information Sciences and Systems (CISS), Princeton, NJ, Mar. 2006, 1194–1099
Lyu S. Natural Image Statistics for Digital Image Forensics. Ph.D. thesis, Department of Computer Science, Dartmouth College, Hanover, NH, 2005
Lyu S, Farid H. How realistic is photorealistic? IEEE Trans. Signal Processing, 2005, 53(2): 845–850
Ng T T, Chang S F, Hsu J, et al. Physics-motivated features for distinguishing photographic images and computer graphics. In: Proceedings of ACM Multimedia, Singapore, Nov. 2005, 5: 239–248
Dehnie S, Sencar T H, Memon N. Digital image forensics for identifying computer generated and digital camera images. In: Proceedings of ICIP’06, Polytechnic University, 2006, 2313–2316
Ng T T, Chang S F, Hsu J, et al. Columbia photographic images and photorealistic computer graphics dataset. ADVENT Technical Report 205-2004-5, Columbia University, Feb. 2005
Ng T T, Chang S F. An online system for classifying computer graphics images from natural photographs. In: Proceedings of SPIE Electronic Imaging, USA, Jan. 2006, 6072:397–405
Lyu S, Rockmore D, Farid H. A digital technique for art authentication. Proc. National Academy of Sciences, 2004, 101(49): 17006–17010
Lyu S, Rockmore D, Farid H. Wavelet analysis for authentication, in Art+Math=X, Boulder, CO, 2005
Farid H, Lyu S. Higher-order wavelet statistics and their application to digital forensics. In: IEEE Workshop on Statistical Analysis in Computer Vision (in conjunction with CVPR), Madison, Wisconsin, 2003
Wei L Y. Texture Synthesis by Fixed Neighborhood Searching. Ph.d. dissertation, Stanford University, Nov. 2001
Criminisi A, Perez P, Toyama K. Region filling and object removal by exemplar-based image inpainting. IEEE Trans. Image Processing, Sept. 2004, 13(9): 1200–1212
Bertalmio M, Sapiro G, Caselles V, et al. Image inpainting. In: Proceedings of ACM SIGGRAPH’2000, 2000, 417–424
Farid H. Creating and detecting doctored and virtual images: Implications to the child pornography prevention act. Technical Report TR2004-518, Department of Computer Science, Dartmouth College, 2004
Wallace G. The jpeg still picture compression standard. IEEE Trans. Consumer Electronics, Feb. 1992, 38(1): xviii–xxxiv
Fan Z, de Queiroz R. Maximum likelihood estimation of jpeg quantization table in the identification of bitmap compression history. In: Proceedings of ICIP’ 00, 10–13 Sept. 2000, 1: 948–951
Fan Z, de Queiroz R. Identification of bitmap compression history: Jpeg detection and quantizer estimation..IEEE Trans. Image Processing, Feb. 2003, 12(2): 230–235
Fridrich J, Goljan M, Du R. Steganalysis based on jpeg compatibility. In: Proceedings of SPIE Electronic Imaging, Multimedia Systems and Applications, 2001, 4518(1): 275–280
Neelamani R, Baraniuk R, de Queiroz R. Compression color space estimation of jpeg images using lattice basis reduction. In: Proceedings of ICIP’ 01, 7–10 Oct. 2001, 1: 890–893
Neelamani R, de Queiroz R, Fan Z, et al. Jpeg compression history estimation for color images. In: Proceedings of ICIP’ 03, 14–17 Sept. 2003, 3: 245–248
Neelamani R, de Queiroz R, Fan Z, et al. Jpeg compression history estimation for color images. IEEE Trans. Image Processing, June 2006, 15(6): 1365–1378
Lukas J, Fridrich J. Estimation of primary quantization matrix in double compressed jpeg images. In: Proceedings of DFRWS, Cleveland, OH, USA, 5–8 Aug. 2003
Popescu A. Statistical Tools for Digital Image Forensics. Ph.D. thesis, Department of Computer Science, Dartmouth College, Hanover, NH, 2005
Fu D D, Shi Y Q, Su W. A generalized Benford’s law for jpeg coefficients and its applications in image forensics. In: Proceedings of SPIE Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents, 2007 (in press), 6505: 58
Schaefer G, Stich M. Ucid: an uncompressed color image database. In: Proceedings of SPIE Electronic Imaging, Storage and Retrieval Methods and Applications for Multimedia, 2003, 5307(1): 472–480
Luo W Q, Qu Z H, Huang J W, et al. A novel method for detecting cropped and recompressed image block. In: Proceedings of ICASSP’07, 2007(in press)
Fridrich J, Soukal D, Lukas J. Detection of copy-move forgery in digital images. In: Processings of DFRWS, Cleveland, OH, USA, 5–8 Aug. 2003
Popescu A, Farid H. Exposing digital forgeries by detecting duplicated image regions, Tech. Rep. TR2004-515, Department of Computer Science, Dartmouth College, 2004
Luo W Q, Huang J W, Qiu G P. Robust detection of region-duplication forgery in digital image. In: Proceedings of ICPR’06, 20–24 Aug. 2006, 4: 746–749
Ng T T, Chang S F. A model for image splicing. In: Proceedings of ICIP’ 04, 24–27 Oct. 2004, 2: 1169–1172
Ng T T, Chang S F. A data set of authentic and spliced image blocks. ADVENT Technical Report 203-2004-3, Columbia University, June 2004
Farid H. A picture tells a thousand lies, New Scientist, 6 Sept. 2003, 179(2411): 38–41
Ng T T, Chang S F, Sun Q. Blind detection of photomontage using higher order statistics. In: Proceedings of International Symposium on Circuits and Systems, 23–26 May 2004, 5: 688–691
Ng T T, Chang S F. Blind detection of digital photomontage using higher order statistics. ADVENT Technical Report 201-2004-1, Columbia University, June 2004
Johnson M, Farid H. Exposing digital forgeries by detecting inconsistencies in lighting. In: Proceedings of ACM the 7th workshop on Multimedia and Security Workshop, New York, NY, 2005, 1–10
Hsu Y F, Chang S F. Detecting image splicing using geometry invariants and camera characteristics consistency. In: Proceedings of ICME’06, Toronto, Canada, July 2006
Chen W, Shi Y Q, Su W. Image splicing detection using 2-d phase congruency and statistical moments of characteristic function. In: Proceedings of SPIE Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents, 2007(to appear), 6505: 27
Farid H. Detecting digital forgeries using bispectral analysis. Tech. Rep. AIM-1657, AI Lab, Massachusetts Institute of Technology, 1999
Ng T T, Chang S F, Tsui M P. Camera response function estimation from a single-channel image using differential invariants. ADVENT Technical Report 216-2006-2, Columbia University, Mar. 2006
Shi Y Q, Xuan G R, Zou D, et al. Image steganalysis based on moments of characteristic functions using wavelet decomposition, prediction-error image, and neural network. In: Proceedings of ICME’05, 6–8 July 2005, 4
Swaminathan A, Wu M, Liu K J R. Image tampering identification using blind deconvolution. In: Proceedings of ICIP’ 06, Atlanta, GA, Oct. 2006, 2311–2314
Grossberg M, Nayar S. What is the space of camera response functions?. In: Proceedings of CVPR’03, 18–20 June 2003, 2: 602–609
Farid H. Blind inverse gamma correction, IEEE Trans. Image Processing, 2001, 10(10): 1428–1433
Lin S, Zhang L. Determining the radiometric response function from a single grayscale image. In: Proceedings of CVPR’05, 20–25 June 2005, 2: 66–73
Lin Z C, Wang R R, Tang X O, et al. Detecting doctored images using camera response normality and consistency. In: Proceedings of CVPR’05, 20–25 June 2005, 1: 1087–1092
Johnson M, Farid H. Exposing digital forgeries through chromatic aberration. In: Proceedings of ACM the 8th workshop on Multimedia and Security Workshop, Geneva, Switzerland, 2006, 48–55
Willson R, Shafer S. What is the center of the image? In: Proceedings of CVPR’93, 15–17 June 1993, 670–671
Popescu A, Farid H. Statistical tools for digital forensics. In: 6th International Workshop on Information Hiding, Toronto, Canada, May 2004, 3200: 128–147
Popescu A, Farid H. Exposing digital forgeries by detecting traces of re-sampling. IEEE Trans. Signal Processing, 2005, 53(2): 758–767
Wang W, Farid H. Exposing digital forgeries in video by detecting double mpeg compression. In: Proceedings of ACM the 8th workshop on Multimedia and Security Workshop, Geneva, Switzerland, 2006, 37–47
Farid H. Exposing digital forgeries in scientific images. In: Proceedings of ACM the 8th workshop on Multimedia and Security Workshop, Geneva, Switzerland, 2006, 29–36
Avcibas I, Bayram S, Memon N, Ramkumar M, Sankur B. A classifier design for detecting image manipulations. In: Proceedings of ICIP’04, 24–27 Oct. 2004, 4: 2645–2648
Johnson M, Farid H. Metric measurements on a plane from a single image. Technical Report TR2006-579, Department of Computer Science, Dartmouth College, 2006
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Luo, W., Qu, Z., Pan, F. et al. A survey of passive technology for digital image forensics. Front. Comput. Sc. China 1, 166–179 (2007). https://doi.org/10.1007/s11704-007-0017-0
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DOI: https://doi.org/10.1007/s11704-007-0017-0