It is indisputable that digital image has brought a revolutionary impact to our daily lives. With the convenience, low-cost, reproducibility, and ease of storage, the amount of digital image has surpassed and overwhelmed that of analog photos from all history of photography. However, while in the age of analog photography, images may imply truth, nowadays tampered images are being produced with an alarming frequency. With the aid of powerful technology and availability of image manipulating software, creating fake image has become easier than ever and digital image forgery is not an uncommon thing today. That leads to a growing concern about the authenticity of digital image.

This concern has motivated a new research trend of detecting traces of manipulation that has been done to a digital image. Most of the works are based on the assumption that, a tampered image, even created by a skilled forger and leaves no visual sign of being tampered, still leaves some traces in statistical properties. By properly modeling and extracting these statistical parameters, the tampered images can be distinguished from the authentic ones.

A survey for the obtained results in the field is often desired as a fundamental background for further research in a thesis. Such a study for tampering techniques and existing state-of-the-art forensics techniques has been carried out and summarized in Chapters 1 and 2. From the knowledge gathered, Chapters 3 and 4 specifically investigate two techniques of tampering detection. One technique is to deal with resaved JPEG images, often for detecting splicing tamper; the other one is to deal with another very common type of forgery: duplicate a portion and paste it to another area of the image. In Chapter 3, we also describe an extended application derived from forensics, which is secret communication and semi-fragile watermark by controlling quantization tables in JPEG compression scheme. In Chapter 4, we propose a scheme for detecting copy-moved tampering by finding matched robust features extracted from the well-known algorithms SIFT (Scale Invariant Features Transform) and SURF (Speeded Up Robust Features). In comparison with other schemes, this scheme excels in detecting performance since it is capable of detecting duplicated regions even with the presence of noise, rotation, scaling and blurring, without having to store and lexicographically sort a huge matrix of features extract from overlapping blocks as in traditional approaches.

[1] H. Bay, T. Tuytelaars, and L. V. Gool, “SURF: Speeded up robust features,” in In ECCV, 2006, pp. 404–417. (Cited on pages 57, 62, and 69.)
[2] S. Bayram, H. Sencar, N. Memon, and I. Avcibas, “Source camera identification based on cfa interpolation,” in Proc. IEEE Int. Conf. Image Processing
ICIP 2005, vol. 3, 2005. (Cited on page 23.)
[3] S. Bayram, H. Sencar, and N. Memon, “An efficient and robust method for detecting copy-move forgery,” in Acoustics, Speech and Signal Processing,
2009. ICASSP 2009. IEEE International Conference on, April 2009, pp. 1053–1056. (Cited on pages 29 and 56.)
[4] G. Cao, Y. Zhao, and R. Ni, “Detection of image sharpening based on histogram aberration and ringing artifacts,” in Multimedia and Expo, 2009.
ICME 2009. IEEE International Conference on, Jul. 2009, pp. 1026–1029. (Cited on page 33.)
[5] W. Chen, Y. Shi, and G. Xuan, “Identifying computer graphics using HSV color model and statistical moments of characteristic functions,” in
Multimedia and Expo, 2007 IEEE International Conference on, July 2007, pp. 1123–1126. (Cited on page 30.)
[6] W. Chen, Y. Q. Shi, and W. Su, “Image splicing detection using 2-D phase congruency and statistical moments of characteristic function,” in
Proceedings of SPIE: Security and Watermarking of Multimedia Content IX, vol. 6505, 2007, p. 65050R. (Cited on page 26.)
[7] K. Choi, E. Y. Lam, and K. Y. Wong, “Source camera identification using footprints from lens aberration,” in Digital Photography II. SPIE, 2006, p.
60690J. [Online]. Available: http://link.aip.org/link/?PSI/6069/60690J/1 (Cited on page 21.)
[8] I. Cox, M. Miller, J. Bloom, J. Fridrich, and T. Kalker, Digital Watermarking and Steganography, 2nd ed. San Francisco, CA, USA: Morgan Kaufmann
Publishers Inc., 2007. (Cited on page 13.)
[9] A. Dirik and N. Memon, “Image tamper detection based on demosaicing artifacts,” in Image Processing (ICIP), 2009 16th IEEE International Conference on, nov 2009, pp. 1497–1500. (Cited on page 24.)
[10] A. Dirik, S. Bayram, H. Sencar, and N. Memon, “New features to identify computer generated images,” in Image Processing, 2007. ICIP 2007. IEEE
International Conference on, vol. 4, Oct. 2007, pp. IV –433 –IV –436. (Cited on page 30.)
[11] J. Dong, W. Wang, T. Tan, and Y. Shi, “Run-length and edge statistics based approach for image splicing detection,” in Digital Watermarking, ser. Lecture Notes in Computer Science, H.-J. Kim, S. Katzenbeisser, and
A. Ho, Eds. Springer Berlin / Heidelberg, 2009, vol. 5450, pp. 76–87. (Cited on page 26.)
[12] H. Farid, “Exposing digital forgeries from JPEG ghosts,” Information Forensics and Security, IEEE Transactions on, vol. 4, no. 1, pp. 154 –160, Mar.
2009. (Cited on pages 25 and 43.)
[13] H. Farid and S. Lyu, “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. (Cited on page 30.)
[14] H. Farid, “Hany Farid homepage,” http://www.cs.dartmouth.edu/farid/research/digitaltampering/index3.html. (Cited on pages 3, 4, 5, 6, and 9.)
[15] H. Farid and M. J. Bravo, “Image forensic analyses that elude the human visual system,” in Media Forensics and Security II, vol. 7541. SPIE, 2010, p. 754106. (Cited on page 31.)
[16] X. Feng and G. Doërr, “JPEG recompression detection,” in Media Forensics and Security II, N. D. Memon, J. Dittmann, A. M. Alattar, and E. J. D. III, Eds., vol. 7541. SPIE, 2010, p. 75410J. (Cited on page 25.)
[17] C. Fillion and G. Sharma, “Detecting content adaptive scaling of images for forensic applications,” in Media Forensics and Security II, vol. 7541. SPIE, 2010, p. 75410Z. (Cited on pages 27 and 28.)
[18] J. Fridrich, “Image watermarking for tamper detection,” in Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference on, vol. 2, 10 1998, pp. 404–408 vol.2. (Cited on page 13.)
[19] J. Fridrich, M. Goljan, , and M. Du, “Invertible authentication,” in Proceedings of SPIE, Security and Watermarking of Multimedia Contents, 2001.(Cited on page 14.)
[20] J. Fridrich, D. Soukal, and Lukas, “Detection of copy-move forgery in digital images,” Proc. of DFRWS 2003, vol. 1, pp. 90–105, 2003. (Cited on pages 28, 29, and 56.)
[21] D. Fu, Y. Shi, and W. Su, “Detection of image splicing based on Hilbert-Huang transform and moments of characteristic functions with wavelet decomposition,” in Digital Watermarking, ser. Lecture Notes in Computer
Science, Y. Shi and B. Jeon, Eds. Springer Berlin / Heidelberg, 2006, vol. 4283, pp. 177–187. (Cited on page 26.)
[22] A. C. Gallagher, “Detection of linear and cubic interpolation in JPEG compressed images,” in Proceedings of the 2nd Canadian conference on Computer
and Robot Vision, ser. CRV ’05. Washington, DC, USA: IEEE Computer Society, 2005, pp. 65–72. (Cited on page 27.)
[23] Z. J. Geradts, J. Bijhold, M. Kieft, K. Kurosawa, K. Kuroki, and N. Saitoh, “Methods for identi?cation of images acquired with digital cameras,” in
Enabling Technologies for Law Enforcement and Security, S. K. Bramble, E. M. Carapezza, and L. I. Rudin, Eds., vol. 4232. SPIE, 2001, pp. 505–512. (Cited on page 22.)
[24] S. Gholap and P. Bora, “Illuminant colour based image forensics,” in TENCON 2008 - 2008 IEEE Region 10 Conference, Nov. 2008, pp. 1–5. (Cited on page 31.)
[25] T. Gloe, K. Borowka, and A. Winkler, “Ef?cient estimation and large-scale evaluation of lateral chromatic aberration for digital image forensics,” in
Media Forensics and Security II, vol. 7541. SPIE, 2010, p. 754107. (Cited on page 22.)
[26] H. Gou, A. Swaminathan, and M. Wu, “Noise features for image tampering detection and steganalysis,” in Image Processing, 2007. ICIP 2007. IEEE International Conference on, vol. 6, Oct. 2007, pp. VI –97 –VI –100. (Cited
on page 32.)
[27] J.-M. Guo and T.-N. Le, “Secret communication using JPEG double compression,” Signal Processing Letters, IEEE, vol. 17, no. 10, pp. 879–882, Oct. 2010. (Cited on page 54.)
[28] C. Hass, “JPEG tutorial website,” http://www.impulseadventure.com/photo/jpeg-quantization.html, 2010. (Cited on page 42.)
[29] D.-Y. Hsiao and S.-C. Pei, “Detecting digital tampering by blur estimation,” in Proc. of the 1st Int’l Workshop on Systematic Approaches to Digital Forensic
Eng. Washington, DC, USA: IEEE Computer Society, 2005, p. 264. (Cited on page 32.)
[30] Y.-F. Hsu and S.-F. Chang, “Image splicing detection using camera re-
sponse function consistency and automatic segmentation,” in Multimedia and Expo, 2007 IEEE International Conference on, Jul. 2007, pp. 28 –31. (Cited
on page 26.)
[31] Y. Huang and Y. Long, “Demosaicking recognition with applications in digital photo authentication based on a quadratic pixel correlation model,” in Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE
Conference on, June 2008, pp. 1 –8. (Cited on page 24.)
[32] W. Jing and Z. Hongbin, “Exposing digital forgeries by detecting traces of image splicing,” in Signal Processing, 2006 8th International Conference on,
vol. 2, 2006, pp. 16–20. (Cited on page 26.) [33] M. K. Johnson and H. Farid, “Exposing digital forgeries through
chromatic aberration,” in Proceedings of the 8th workshop on Multimedia and security. New York, NY, USA: ACM, 2006, pp. 48–55. [Online]. Available:
http://doi.acm.org/10.1145/1161366.1161376 (Cited on page 22.)
[34] M. Johnson and H. Farid, “Exposing digital forgeries in complex light-
ing environments,” IEEE Transactions on Information Forensics and Security,
vol. 3, no. 2, pp. 450–461, 2007. (Cited on page 30.)
[35] N. Khanna, G.-C. Chiu, J. Allebach, and E. Delp, “Forensic techniques for classifying scanner, computer generated and digital camera images,”
in Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on, 2008, pp. 1653 –1656. (Cited on page 30.)
[36] M. Kirchner, “Fast and reliable resampling detection by spectral analysis of fixed linear predictor residue,” in Proceedings of the 10th ACM workshop
on Multimedia and security. New York, NY, USA: ACM, 2008, pp. 11–20. (Cited on page 27.)
[37] D. Kundur and D. Hatzinakos, “Digital watermarking for telltale tamper
proofing and authentication,” Proceedings of the IEEE, vol. 87, no. 7, pp.
1167–1180, Jul. 1999. (Cited on page 15.)
[38] A. Leykin and F. Cutzu, “Differences of edge properties in photographs
and paintings,” in Image Processing, 2003. ICIP 2003. Proceedings. 2003
International Conference on, vol. 3, Sep. 2003, pp. III – 541–4 vol.2. (Cited
on page 30.)
[39] G. Li, Q. Wu, D. Tu, and S. Sun, “A sorted neighborhood approach for
detecting duplicated regions in image forgeries based on DWT and SVD,”
in Multimedia and Expo, 2007 IEEE International Conference on, July 2007,
pp. 1750–1753. (Cited on page 29.)
[40] H. J. Lin, C. W. Wang, and Y. T. Kao, “Fast copy-move forgery detection,”
WSEAS Trans. Sig. Proc., vol. 5, pp. 188–197, May 2009. (Cited on pages 28
and 56.)
[41] Z. Lin, R. Wang, X. Tang, and H.-Y. Shum, “Detecting doctored images
using camera response normality and consistency,” in Computer Vision
and Pattern Recognition, CVPR ’05. IEEE Comp. Soc. Conf. on, vol. 1, 2005,
pp. 1087–1092 vol. 1. (Cited on page 26.)
[42] D. G. Lowe, “Distinctive image features from scale-invariant keypoints,”
Int. Journal of Computer Vision, vol. 60, pp. 91–110, November 2004. (Cited
on pages 57, 58, 69, and 73.)
[43] J. Lukas, J. Fridrich, and M. Goljan, “Digital camera identi?cation from
sensor pattern noise,” Information Forensics and Security, IEEE Transactions
on, vol. 1, no. 2, pp. 205 – 214, June 2006. (Cited on page 23.)
[44] J. Lukas and J. Fridrich, “Estimation of primary quantization matrix in
double compressed JPEG images,” in Proc. of DFRWS, 2003. (Cited on
page 25.)
[45] W. Luo, Z. Qu, J. Huang, and G. Qiu, “A novel method for detecting
cropped and recompressed image block,” in Acoustics, Speech and Signal
Processing, 2007. ICASSP 2007. IEEE International Conference on, vol. 2, Apr.
2007, pp. II–217 –II–220. (Cited on page 25.)
[46] S. Lyu and H. Farid, “Steganalysis using color wavelet statistics and one-
class support vector machines,” in SPIE Symposium on Electronic Imaging,
2004, pp. 35–45. (Cited on page 53.)
[47] B. Mahdian and S. Saic, “Blind authentication using periodic properties
of interpolation,” Information Forensics and Security, IEEE Transactions on,
vol. 3, no. 3, pp. 529 –538, Sep. 2008. (Cited on page 27.)
[48] ——, “Detection of resampling supplemented with noise inconsistencies
analysis for image forensics,” in Computational Sciences and Its Applications,
2008. ICCSA ’08. International Conference on, Jul. 2008, pp. 546 –556. (Cited
on page 32.)
[49] ——, “Detection of copy-move forgery using a method based on blur
moment invariants,” Forensic Science International, vol. 171, no. 2-3, pp.
180–189, 2007. (Cited on pages 28, 29, 56, and 76.)
[50] K. Mikolajczyk and C. Schmid, “A performance evaluation of local de-
scriptors,” IEEE Transactions on Pattern Analysis & Machine Intelligence,
vol. 27, no. 10, pp. 1615–1630, 2005. (Cited on page 57.)
[51] A. Myrna, M. Venkateshmurthy, and C. Patil, “Detection of region dupli-
cation forgery in digital images using wavelets and log-polar mapping,”
in Computational Intelligence and Multimedia Appl., Int. Conf. on, vol. 3, Dec.
2007, pp. 371–377. (Cited on pages 29 and 56.)
[52] T.-T. Ng, “Camera response function signature for digital forensics - part
II: Signature extraction,” in Info. Forensics and Security. WIFS 2009. 1st
IEEE Int Workshop on, Dec. 2009, pp. 161 –165. (Cited on page 26.)
[53] T.-T. Ng and M.-P. Tsui, “Camera response function signature for digital
forensics - part I: Theory and data selection,” in Info. Forensics and Security.
WIFS 2009. 1st IEEE Int Workshop on, Dec. 2009, pp. 156 –160. (Cited on
page 26.)
[54] T.-T. Ng, S.-F. Chang, and Q. Sun, “Blind detection of photomontage
using higher order statistics,” in Circuits and Systems, 2004. ISCAS ’04.
Proceedings of the 2004 International Symposium on, vol. 5, May 2004, pp.
V–688 – V–691 Vol.5. (Cited on page 26.)
[55] T.-T. Ng, S.-F. Chang, J. Hsu, L. Xie, and M.-P. Tsui, “Physics-motivated
features for distinguishing photographic images and computer graphics,”
in Proceedings of the 13th annual ACM international conference on Multimedia,
ser. MULTIMEDIA ’05. New York, NY, USA: ACM, 2005, pp. 239–248.
(Cited on pages 26 and 29.)
[56] T.-T. Ng, S.-F. Chang, and M.-P. Tsui, “Lessons learned from online
classi?cation of photo-realistic computer graphics and photographs,” in
Signal Processing Applications for Public Security and Forensics. SAFE ’07.
IEEE Workshop on, 2007, pp. 1–6. (Cited on page 30.)
[57] W. B. Pennebaker and J. L. Mitchell, JPEG: Still Image Data Compression
Standard. New York: Springer, 1993. (Cited on page 34.)
[58] A. P. Pentland, “Finding the illuminant direction,” Journal of Optical
Society American, vol. 72, no. 4, pp. 448–455, Apr. 1982. [Online]. Available:
http://www.opticsinfobase.org/abstract.cfm?URI=josa-72-4-448 (Cited
on page 30.)
[59] T. Pevny and J. Fridrich, “Detection of double-compression in JPEG im-
ages for applications in steganography,” Information Forensics and Security,
IEEE Transactions on, vol. 3, no. 2, pp. 247–258, June 2008. (Cited on
page 25.)
[60] A. Popescu and H. Farid, “Exposing digital forgeries in color ?lter ar-
ray interpolated images,” Signal Processing, IEEE Transactions on, vol. 53,
no. 10, pp. 3948 – 3959, Oct. 2005. (Cited on page 23.)
[61] ——, “Statistical tools for digital forensics,” in 6th Int. Workshop on In-
formation Hiding. Springer-Verlag, Berlin-Heidelberg, 2004, pp. 128–147.
(Cited on pages 25 and 31.)
bibliography 93
[62] ——, “Exposing digital forgeries by detecting duplicated image regions,”
Department of Computer Science, Dartmouth College, Tech. Rep. TR2004–
515, 2004. (Cited on pages 28, 29, and 56.)
[63] ——, “Exposing digital forgeries by detecting traces of resampling,” Signal
Processing, IEEE Transactions on, vol. 53, no. 2, pp. 758–767, Feb. 2005.
(Cited on page 27.)
[64] N. Provos, “Outgess website,” http://www.outguess.org/. (Cited on
page 53.)
[65] G. Sankar, V. Zhao, and Y.-H. Yang, “Feature based classi?cation of
computer graphics and real images,” in Proc. of the IEEE Int. Conf. on
Acoustics, Speech and Signal Processing ICASSP ’09, Washington, DC, USA,
2009, pp. 1513–1516. (Cited on page 30.)
[66] Y. Sutcu, B. Coskun, H. Sencar, and N. Memon, “Tamper detection based
on regularity of wavelet transform coef?cients,” in Image Processing, 2007.
ICIP 2007. IEEE International Conference on, vol. 1, oct 2007, pp. I–397–I–
400. (Cited on page 33.)
[67] H.-K. Tso, D.-C. Lou, K.-P. Wang, and C.-L. Liu, “A lossless secret image
sharing method,” in Intelligent Systems Design and Applications, 2008. ISDA
’08. Eighth International Conference on, vol. 3, Nov. 2008, pp. 616–619. (Cited
on page 45.)
[68] G. Voyatzis and I. Pitas, “Applications of toral automorphisms in image
watermarking,” in Image Processing, 1996. Proceedings., Int. Conf. on, vol. 1,
Sep. 1996, pp. 237–240 vol.2. (Cited on page 45.)
[69] W. Wang, J. Dong, and T. Tan, “Effective image splicing detection based
on image chroma,” in Image Processing (ICIP) 2009, 16th IEEE International
Conference on, Nov. 2009, pp. 1257–1260. (Cited on page 26.)
[70] P. W. Wong, “A public key watermark for image veri?cation and authen-
tication,” in Image Processing, 1998. ICIP 98. Proceedings. 1998 International
Conference on, vol. 1, Oct. 1998, pp. 455–459 vol.1. (Cited on page 14.)
[71] S. Ye, Q. Sun, and E. C. Chang, “Detecting digital image forgeries by
measuring inconsistencies of blocking artifact,” in Multimedia and Expo,
2007 IEEE Int. Conf. on, Jul. 2007, pp. 12–15. (Cited on page 25.)
[72] M. Yeung and F. Mintzer, “An invisible watermarking technique for
image verification,” in Image Processing, 1997. Proceedings., International
Conference on, vol. 2, Oct. 1997, pp. 680–683 vol.2. (Cited on page 15.)
[73] C. Zhang and Z. Hongbin, “Detecting digital image forgeries through
weighted local entropy,” in Signal Processing and Information Technology,
IEEE Int Symposium on, Dec. 2007, pp. 62–67. (Cited on page 33.)
[74] J. Zhang, H. Wang, and Y. Su, “Detection of double-compression in
JPEG2000 images,” in Intelligent Info. Tech. App., 2008, IITA. 2nd Int. Sym-
posium on, vol. 1, dec 2008, pp. 418–421. (Cited on page 25.)
[75] W. Zhang, X. Cao, J. Zhang, J. Zhu, and P. Wang, “Detecting photographic
composites using shadows,” in Multimedia and Expo, 2009 ICME. IEEE Int.
Conf. on, Jul. 2009, pp. 1042 –1045. (Cited on page 31.)
[76] Z. Zhang, G. Wang, Y. Bian, and Z. Yu, “A novel model for splicing
detection,” in Bio-Inspired Computing: Theories and Applications (BIC-TA),
2010 IEEE Fifth International Conference on, Sep. 2010, pp. 962 –965. (Cited
on page 26.)
[77] Q. Zheng and R. Chellappa, “Estimation of illuminant direction, albedo,
and shape from shading,” Pattern Analysis and Machine Intelligence, IEEE
Transactions on, vol. 13, no. 7, pp. 680–702, Jul. 1991. (Cited on page 30.)
[78] L. Zhou, D. Wang, Y. Guo, and J. Zhang, “Blur detection of digital
forgery using mathematical morphology,” in Agent and Multi-Agent Sys-
tems: Technologies and Applications, ser. Lecture Notes in Computer Science.
Springer Berlin / Heidelberg, 2007, vol. 4496, pp. 990–998. (Cited on
page 32.)