Recent Advances in Passive Digital Image Security Forensics: A Brief Review

Xiang Lin, Jian-Hua Li, Shi-Lin Wang, Alan-Wee-Chung Liew, Feng Cheng, Xiao-Sa Huang

Engineering ›› 2018, Vol. 4 ›› Issue (1) : 29-39.

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Engineering ›› 2018, Vol. 4 ›› Issue (1) : 29-39. DOI: 10.1016/j.eng.2018.02.008
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Recent Advances in Passive Digital Image Security Forensics: A Brief Review

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Abstract

With the development of sophisticated image editing and manipulation tools, the originality and authenticity of a digital image is usually hard to determine visually. In order to detect digital image forgeries, various kinds of digital image forensics techniques have been proposed in the last decade. Compared with active forensics approaches that require embedding additional information, passive forensics approaches are more popular due to their wider application scenario, and have attracted increasing academic and industrial research interests. Generally speaking, passive digital image forensics detects image forgeries based on the fact that there are certain intrinsic patterns in the original image left during image acquisition or storage, or specific patterns in image forgeries left during the image storage or editing. By analyzing the above patterns, the originality of an image can be authenticated. In this paper, a brief review on passive digital image forensic methods is presented in order to provide a comprehensive introduction on recent advances in this rapidly developing research area. These forensics approaches are divided into three categories based on the various kinds of traces they can be used to track—that is, traces left in image acquisition, traces left in image storage, and traces left in image editing. For each category, the forensics scenario, the underlying rationale, and state-of-the-art methodologies are elaborated. Moreover, the major limitations of the current image forensics approaches are discussed in order to point out some possible research directions or focuses in these areas.

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Digital image forensics / Image-tampering detection / Multimedia security

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Xiang Lin, Jian-Hua Li, Shi-Lin Wang, Alan-Wee-Chung Liew, Feng Cheng, Xiao-Sa Huang. Recent Advances in Passive Digital Image Security Forensics: A Brief Review. Engineering, 2018, 4(1): 29‒39 https://doi.org/10.1016/j.eng.2018.02.008

References

[1]
A. Peraica. Image science: Iconology, visual culture, and media aesthetics. Leonardo,49 (3) (2016), p. 285
[2]
H. Farid. A survey of image forgery detection. IEEE Signal Proc Mag,26 (2) (2009), pp. 16-25
[3]
G. Zhou, D. Lv. An overview of digital watermarking in image forensics. Proceedings of 2011 Fourth International Joint Conference on Computational Sciences and Optimization; 2011 Apr 15-19; Yunnan, China, IEEE Computer Society, Washington, DC (2011), pp. 332-335
[4]
H. Farid. How to detect faked photos. Am Sci,105 (2) (2017), pp. 77-81
[5]
G.K. Birajdar, V.H. Mankar. Digital image forgery detection using passive techniques: A survey. Digital Invest,10 (3) (2013), pp. 226-245
[6]
K.S. Choi, E.Y. Lam, K.K.Y. Wong. Source camera identification using footprints from lens aberration. N. Sampat, J.M. DiCarlo, R.A. Martin (Eds.), Proceedings of SPIE—Electronic Imaging 2006: Digital Photography II; 2006 Jan 16-19; San Jose, CA, USA, International Society for Optics and Photonics, Bellingham (2006), pp. 172-179
[7]
I. Yerushalmy, H. Hel-Or. Digital image forgery detection based on lens and sensor aberration. Int J Comput Vis,92 (1) (2011), pp. 71-91. DOI: 10.1007/s11263-010-0403-1
[8]
J. Lukas, J. Fridrich, M. Goljan. Digital camera identification from sensor pattern noise. IEEE Trans Inf Foren Sec,1 (2) (2006), pp. 205-214
[9]
N. Kulkarni, V. Mane.Improvements on sensor noise based on source camera identification using GLCM. Proceedings of International Conference on Advances in Science and Technology; 2014 Oct 29-31 ; Ota, Nigeria, International Journal of Computer Applications, New York (2015), pp. 1-4. DOI: 10.17697/ibmrd/2015/v4i2/76769
[10]
A.L. Sandoval Orozco, D.M. Arenas González, J. Rosales Corripio, L.J. García Villalba, J.C. Hernandez-Castro. Source identification for mobile devices, based on wavelet transforms combined with sensor imperfections. Computing,96 (9) (2014), pp. 829-841. DOI: 10.1007/s00607-013-0313-5
[11]
J. Fridrich. Digital image forensics using sensor noise. IEEE Signal Proc Mag,26 (2) (2009), pp. 26-37
[12]
S. Gao, G. Xu, RM. Hu. Camera model identification based on the characteristic of CFA and interpolation. IWDW'11 Proceedings of the 10th International Conference on Digital-Forensics and Watermarking; 2011 Oct 23-26; Atlantic City, NJ, USA, Springer-Verlag, Berlin (2012), pp. 268-280. DOI: 10.1007/978-3-642-32205-1_22
[13]
P. Prasad. Image forgery localization via CFA based feature extraction and Poisson matting. Int J Sci Res,3 (10) (2014), pp. 1273-1278
[14]
Y. Katre, G.S. Chandel. Image forgery detection using analysis of CFA artifacts. Int J Adv Technol Eng Sci,2 (1) (2014), pp. 381-389
[15]
J. Lukas, J. Fridrich.Estimation of primary quantization matrix in double compressed JPEG images. Proceedings of Digital Forensic Research Workshop; 2003 Aug 5-8; Cleveland, OH, USA (2003), pp. 5-8
[16]
D. Fu, Y.Q. Shi, W. Su. A generalized Benford’s law for JPEG coefficients and its applications in image forensics. E.J. Delp, P.W. Wong (Eds.), Proceedings of SPIE—Electronic Imaging 2007: Security, Steganography, and Watermarking of Multimedia Contents IX; 2007 Jan 28-Feb 1; San Jose, CA, USA, International Society for Optics and Photonics, Bellingham (2007). 65051L1-11
[17]
T. Pevny, J. Fridrich. Detection of double-compression in JPEG images for applications in steganography. IEEE Trans Inf Foren Sec,3 (2) (2008), pp. 247-258
[18]
H. Farid. Exposing digital forgeries from JPEG ghosts. IEEE Trans Inf Foren Sec,4 (1) (2009), pp. 154-160
[19]
Z. Lin, J. He, X. Tang, C.K. Tang. Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis. Pattern Recognit,42 (11) (2009), pp. 2492-2501
[20]
F. Huang, J. Huang, Y.Q. Shi. Detecting double JPEG compression with the same quantization matrix. IEEE Trans Inf Foren Sec,5 (4) (2010), pp. 848-856
[21]
J. Yang, J. Xie, G. Zhu, S. Kwong, Y.Q. Shi. An effective method for detecting double JPEG compression with the same quantization matrix. IEEE Trans Inf Foren Sec,9 (11) (2014), pp. 1933-1942
[22]
W. Luo, Z. Qu, J. Huang, G. Qiu.A novel method for detecting cropped and recompressed image block. Proceedings of IEEE International Conference on Acoustic, Speech and Signal Processing; 2007 Apr 15-20; Honolulu, HI, USA, IEEE, Piscataway (2007), pp. 217-220
[23]
Y.L. Chen, C.T. Hsu. Detecting recompression of JPEG images via periodicity analysis of compression artifacts for tampering detection. IEEE Trans Inf Foren Sec,6 (2) (2011), pp. 396-406
[24]
Z. Qu, W. Luo, J. Huang. A convolutive mixing model for shifted double JPEG compression with application to passive image authentication. Proceedings of IEEE International Conference on Acoustic, Speech and Signal Processing; 2008 Mar 30-Apr 4; Las Vegas, NV, USA, IEEE, Piscataway (2008), pp. 1661-1664. DOI: 10.1109/ICASSP.2008.4517946
[25]
T. Bianchi, A. Piva. Detection of nonaligned double JPEG compression based on integer periodicity maps. IEEE Trans Inf Foren Sec,7 (2) (2012), pp. 842-848
[26]
T. Bianchi, A. Piva. Image forgery localization via block-grained analysis of JPEG artifacts. IEEE Trans Inf Foren Sec,7 (3) (2012), pp. 1003-1017
[27]
S.L. Wang, A.W.C. Liew, S.H. Li, Y.J. Zhang, J.H. Li. Detection of shifted double JPEG compression by an adaptive DCT coefficient model. EURASIP J Adv Signal Process, 2014 (2014), p. 101
[28]
M.K. Johnson, H. Farid.Exposing digital forgeries by detecting inconsistencies in lighting. Proceedings of the 7th Workshop on Multimedia and Security; 2005 Aug 1-2 ; New York, NY, USA, ACM Press, New York (2005), pp. 1-10. DOI: 10.1145/1073170.1073171
[29]
M.K. Johnson, H. Farid. Exposing digital forgeries through specular highlights on the eye. Proceedings of the 9th International Conference on Information Hiding; 2007 Jun 11-13; Saint Malo, France, Springer-Verlag, Berlin (2007), pp. 311-325. DOI: 10.1007/978-3-540-77370-2_21
[30]
M.K. Johnson, H. Farid. Exposing digital forgeries in complex lighting environments. IEEE Trans Inf Foren Sec,2 (3) (2007), pp. 450-461
[31]
E. Kee, H. Farid. Exposing digital forgeries from 3-D lighting environments. Proceedings of 2010 IEEE International Workshop on Information Forensics and Security; 2010 Dec 12-15; Seattle, WA, USA, IEEE, Piscataway (2010), pp. 1-6
[32]
P. Nillius, J.O. Eklundh.Automatic estimation of the projected light source direction. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2001 Dec 8-14; Kauai, HI, USA, IEEE, Piscataway (2001), pp. 1076-1083
[33]
J.J. Koenderink, A.J. van Doorn, S.C. Pont. Light direction from shad(ow)ed random Gaussian surfaces. Perception,33 (12) (2004), pp. 1405-1420. DOI: 10.1068/p5287
[34]
W. Zhang, X. Cao, J. Zhang, J. Zhu, P. Wang. Detecting photographic composites using shadows. Proceedings of 2009 IEEE International Conference on Multimedia and Expo; 2009 Jun 28-Jul 3; New York, NY, USA, IEEE, Piscataway (2009), pp. 1042-1045. DOI: 10.1109/GSIS.2009.5408013
[35]
W. Fan, K. Wang, F. Cayre, Z. Xiong. 3D lighting-based image forgery detection using shape-from-shading. Proceedings of the 20th European Signal Processing Conference; 2012 Aug 27-31; Bucharest, Romania, IEEE, Piscataway (2012), pp. 1777-1781
[36]
A.C. Bovik, T.S. Huang, D.C. Munson. The effect of median filtering on edge estimation and detection. IEEE Trans Pattern Anal Mach Intell,9 (2) (1987), pp. 181-194
[37]
A.C. Bovik. Streaking in median filtered images. IEEE Trans Acoust Speech Signal Process,35 (4) (1987), pp. 493-503
[38]
M. Kirchner, J. Fridrich. On detection of median filtering in digital images. Proceedings of SPIE—Electronic Imaging 2010: Media Forensics and Security II; 2010 Jan 17-21; San Jose, CA, USA, International Society for Optics and Photonics, Bellingham (2010), pp. 7541101-7541112
[39]
G. Cao, Y. Zhao, R. Ni, L. Yu, H. Tian. Forensic detection of median filtering in digital images. Proceedings of 2010 IEEE International Conference on Multimedia and Expo; 2010 Jul 19-23; Singapore, Singapore, IEEE, Piscataway (2010), pp. 89-94. DOI: 10.1109/ICME.2010.5583869
[40]
H.D. Yuan. Blind forensics of median filtering in digital images. IEEE Trans Inf Foren Sec,6 (4) (2011), pp. 1335-1345
[41]
C. Chen, J. Ni. Median filtering detection using edge based prediction matrix. Y.Q. Shi, H.J. Kim, F. Pérez-González (Eds.), Proceeding of 10th International Workshop on Digital Forensics and Watermarking; 2011 Oct 23-26; Atlantic City, NJ, USA, Springer-Verlag, Berlin (2012), pp. 361-375. DOI: 10.1007/978-3-642-32205-1_29
[42]
X. Kang, M.C. Stamm, A. Peng, K.J. Ray Liu. Robust median filtering forensics using an autoregressive model. IEEE Trans Inf Foren Sec,8 (9) (2013), pp. 1456-1468
[43]
C. Chen, J. Ni, J. Huang. Blind detection of median filtering in digital images: A difference domain based approach. IEEE Trans Image Process,22 (12) (2013), pp. 4699-4710
[44]
Y. Zhang, S. Li, S. Wang, Y.Q. Shi. Revealing the traces of median filtering using high-order local ternary patterns. IEEE Signal Proc Lett,21 (3) (2014), pp. 275-279
[45]
J. Chen, X. Kang, Y. Liu, Z. Jane Wang. Median filtering forensics based on convolutional neural networks. IEEE Signal Proc Lett,22 (11) (2015), pp. 1849-1853
[46]
F. Ding, G. Zhu, J. Yang, J. Xie, Y.Q. Shi. Edge perpendicular binary coding for USM sharpening detection. IEEE Signal Proc Lett,22 (3) (2015), pp. 327-331
[47]
G. Cao, Y. Zhao, R. Ni. Detection of image sharpening based on histogram aberration and ringing artifacts. Proceedings of the 2009 IEEE International Conference on Multimedia and Expo; 2009 Jun 28-Jul 3; New York, NY, USA, IEEE, Piscataway (2009), pp. 1026-1029
[48]
G. Cao, Y. Zhao, R. Ni, A.C. Kot. Unsharp masking sharpening detection via overshoot artifacts analysis. IEEE Signal Proc Lett,18 (10) (2011), pp. 603-606
[49]
F. Ding, G. Zhu, Y.Q. Shi. A novel method for detecting image sharpening based on local binary pattern. Y. Shi, H.J. Kim, F. Pérez-González (Eds.), Proceedings of 12th International Workshop on Digital Forensics and Watermarking; 2013 Oct 1-4; Auckland, New Zealand, Springer-Verlag, Berlin (2013), pp. 180-191
[50]
A.J. Fridrich, B.D. Soukal, A.J. Lukas. Detection of copy-move forgery in digital images. Int J,3 (2) (2003), pp. 652-663
[51]
A.C. Popescu, H. Farid. Exposing digital forgeries by detecting duplicated image regions. Technical report. Department of Computer Science, Dartmouth College, Hanover (2004). Report No.: TR2004-515
[52]
S. Bayram, H.T. Sencar, N. Memon. An efficient and robust method for detecting copy-move forgery. Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing; 2009 Apr 19-24; Taipei, Taiwan, China, IEEE Computer Society, Washington, DC (2009), pp. 1053-1056. DOI: 10.1109/ICASSP.2009.4959768
[53]
W. Li, N. Yu. Rotation robust detection of copy-move forgery. Proceedings of 2010 IEEE International Conference on Image Processing; 2010 Sep 26-29; Hong Kong, China, IEEE, Piscataway (2010), pp. 2113-2116. DOI: 10.1109/ICIP.2010.5652519
[54]
M. Zandi, A. Mahmoudi-Aznaveh, A. Mansouri.Adaptive matching for copy-move Forgery detection. Proceedings of 2014 IEEE International Workshop on Information Forensics and Security; 2014 Dec 3-5; Atlanta, GA, USA, IEEE, Piscataway (2014), pp. 119-124
[55]
V. Christlein, C. Riess, J. Jordan, C. Riess, E. Angelopoulou. An evaluation of popular copy-move forgery detection approaches. IEEE Trans Inf Foren Sec,7 (6) (2012), pp. 1841-1854
[56]
A.C. Popescu, H. Farid. Exposing digital forgeries by detecting traces of resampling. IEEE Trans Signal Process,53 (2) (2005), pp. 758-767
[57]
A.C. Gallagher.Detection of linear and cubic interpolation in JPEG compressed images. Proceedings of the 2nd Canadian Conference on Computer and Robot Vision; 2005 May 9-11 ; Victoria, BC, Canada, IEEE Computer Society, Washington, DC (2005), pp. 65-72. DOI: 10.1109/CRV.2005.33
[58]
B. Mahdian, S. Saic. Blind authentication using periodic properties of interpolation. IEEE Trans Inf Foren Sec,3 (3) (2008), pp. 529-538
[59]
M. Kirchner, T. Gloe.On resampling detection in re-compressed images. Proceedings of the 1st IEEE International Workshop on Information Forensics and Security; 2009 Dec 6-9 ; London, UK, IEEE, Piscataway (2009), pp. 21-25. DOI: 10.1109/WIFS.2009.5386489
[60]
D. Vázquez-Padín, P. Comesana, F. Pérez-González.An SVD approach to forensic image resampling detection. Proceedings of the 23rd European Signal Processing Conference; 2015 Aug 31-Sep 4; Nice, France, IEEE, Piscataway (2015), pp. 2112-2116
[61]
I. Avcibas, S. Bayram, N. Memon, M. Ramkumar, B. Sankur.A classifier design for detecting image manipulations. Proceedings of the International Conference on Image Processing; 2004 Oct 24-27 ; Singapore, Singapore, IEEE, Piscataway (2004), pp. 2645-2648. DOI: 10.1109/ICIP.2004.1421647
[62]
Y.Q. Shi, C. Chen, W. Chen.A natural image model approach to splicing detection. Proceedings of the 9th Workshop on Multimedia and Security; 2007 Sep 20-21; Dallas, TX, USA, ACM Press, New York (2007), pp. 51-62. DOI: 10.1145/1288869.1288878
[63]
Ng TT, Hsu J, Chang SF. Columbia image splicing detection evaluation dataset [Internet]. Available from: http://www.ee.columbia.edu/ln/dvmm/downloads/AuthSplicedDataSet/dlform.html.
[64]
W. Wang, J. Dong, T. Tan.Effective image splicing detection based on image chroma. Proceedings of the 16th IEEE International Conference on Image Processing; 2009 Nov 7-10; Cairo, Egypt, IEEE, Piscataway (2009), pp. 1257-1260
[65]
Z. He, W. Lu, W. Sun, J. Huang. Digital image splicing detection based on Markov features in DCT and DWT domain. Pattern Recognit,45 (12) (2012), pp. 4292-4299
[66]
X. Zhao, S. Wang, S. Li, J. Li. Passive image-splicing detection by a 2-D noncausal Markov model. IEEE Trans Circuits Syst Video Techn,25 (2) (2015), pp. 185-199
[67]
B. Bayar, M.C. Stamm.A deep learning approach to universal image manipulation detection using a new convolutional layer. Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security; 2016 Jun 20-22; Vigo, Spain, ACM Press, New York (2016), pp. 5-10. DOI: 10.1145/2909827.2930786
[68]
L. Bondi, D. Güera, L. Baroffio, P. Bestagini, E.J. Delp, S. Tubaro. A preliminary study on convolutional neural networks for camera model identification. Proceedings of IS&T International Symposium on Electronic Imaging: Media Watermarking, Security, and Forensics; 2017 Jan 29-Feb 2; San Francisco, CA, USA, Society for Imaging Science and Technology, Washington, DC (2017), pp. 67-76
[69]
B. Bayar, M.C. Stamm.On the robustness of constrained convolutional neural networks to JPEG post-compression for image resampling detection. Proceedings of 2017 IEEE International Conference on Acoustics, Speech and Signal Processing; 2017 Mar 5-9 ; New Orleans, LA, USA, IEEE, Piscataway (2017), pp. 2152-2156. DOI: 10.1109/ICASSP.2017.7952537
[70]
J. Chen, X. Kang, Y. Liu, Z.J. Wang. Median filtering forensics based on convolutional neural networks. IEEE Signal Proc Lett,22 (11) (2015), pp. 1849-1853
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