Fast Copy-Move Forgery Detection

Transcription

1 Fast Copy-Move Forgery Detecton HWEI-JEN LIN 1, CHUN-WEI WANG 1 and YANG-TA KAO 2 1 Department of Informaton Engneerng and Computer Scence Tamkang Unversty 2 Department of Network Informaton Technology Chhlee Insttute of Technology Yng-Chuan Road, Tamsu Tape, Tawan, ROC Wun-Hua Road, Sec. 1, Bancao, Tape, Tawan, ROC @mal.tku.edu.tw, 2 ydkao@mal.chhlee.edu.tw, Abstract: - Ths paper proposes a method for detectng copy-move forgery over mages tampered by copy-move. To detect such forgeres, the gven mage s dvded nto overlappng blocks of equal sze, feature for each block s then extracted and represented as a vector, all the extracted feature vectors are then sorted usng the radx sort. The dfference (shft vector) of the postons of every par of adjacent feature vectors n the sortng lst s computed. The accumulated number of each of the shft vectors s evaluated. A large accumulated number s consdered as possble presence of a duplcated regon, and thus all the feature vectors correspondng to the shft vectors wth large accumulated numbers are detected, whose correspondng blocks are then marked to form a tentatve detected result. Fnally, the medum flterng and connected component analyss are performed on the tentatve detected result to obtan the fnal result. Compared wth other methods, employng the radx sort makes the detecton much more effcent wthout degradaton of detecton qualty. Key-Words: - Forgery Detecton, Copy-move Forgery, Sngular Value Decomposton (SVD), Prncpal Component Analyss (PCA), Lexcographcal Sort, Scale Invarant Feature Transform (SIFT) Descrptors, Log-polar Coordnates, Radx Sort, Connected Component Analyss 1 Introducton Art forgery dates back more than two-thousand years. Wth the wdespread use of Internet and avalablty of powerful mage processng and edtng software, dgtal mages are easy to acqure through nternet and to manpulate and edt. For example, the mage Fonda Speak To Vetnam Veterans At Ant-War Rally shown n Fg. 1 was syntheszed usng the two mages shown n Fg.s 1 and 1. For protectng copyrght and preventng forgery or alteraton of document wth malcous ntentons, the tasks of forgery detecton become more and more urgent. Recently, varous technques for temper or forgery detecton or even recovery have been proposed n the lterature. Some technques have been proposed for mage tamper detecton and recovery. Varous watermark technques [1][2][3][4][5][6] have been proposed n recent years, whch can be used not only for authentcaton, but also for beng an evdence for the tamper detecton. Wang et al. [7] and Ln et al. [8] both embedded watermarks consstng of the authentcaton data and the recovery data nto mage blocks for mage tamper detecton and recovery n the future. An example of mage tamper detecton and recovery gven by Ln et al. [8] s shown n Fg. 2. L et al. [9] transformed the mage from ts spatal doman to the frequency doman based on the DWT, extracted some nformaton from the frequency doman as the egenvalue of ths mage, and then hd ths egenvalue n the mddle frequency band of the frequency doman as an evdence for the tamper detecton. The egenvalue hdden n the mage could be used to recover ths mage The drawback of watermark technques s that one must embed a watermark nto the mage frst. Many other technques that work n the absence of any dgtal watermark or sgnature have been proposed. Popescu [10] detected resamplng (e.g., scalng or rotatng) based on statstcal correlatons. E. S. Gop et al. [11] exploted the property of correlaton by usng Auto Regressve coeffcents as the feature ISSN: Issue 5, Volume 5, May 2009

2 vector for dentfyng the locaton of dgtal forgery n a sample mage. The major weakness of ths approach s that t s only applcable to uncompressed TIFF mages, and JPEG and GIF mages wth mnmal compresson. Some researchers [12][13][14][15] estmated lght source drecton [16] and used lghtng nconsstences for revealng traces of dgtal tamperng. Defects of cameras such as chromatc aberraton [17][18] and sensor pattern nose [19][20][21], and the color flter arrays the cameras use for nterpolatng colors can be also used to detect forgeres [22]. Fg. 1.. A syntheszed mage Fonda Speak To Vetnam Veterans At Ant-War Rally ; &. orgnal mages. et al. [25] appled DWT to the gven mage, and used SVD on fxed-sze blocks of low-frequency component n wavelet sub-band to yeld a reduced dmenson representaton, then lexcographcally sorted the SV vectors to detect duplcated mage blocks. A. C. Popescu et al. [24] appled a prncpal component analyss (PCA) on small fxed-sze mage blocks to yeld a reduced dmenson representaton. Duplcated regons are then detected by lexcographcally sortng all of the mage blocks. W. Luo et al. [26] also frst dvded an mage nto small overlapped blocks and extracted block characterstcs vector, and then compared the smlarty of these blocks to dentfy possble duplcated regons. A. N. Myna et al. [27] presented an approach based on the applcaton of wavelet transform that detects and performed exhaustve search to dentfy the smlar blocks n the mage by mappng them to log-polar coordnates and usng phase correlaton as the smlarty crteron. H. Huang et al. [28] frst extracted SIFT descrptors of an mage, whch are nvarant to changes n llumnaton, rotaton, scalng etc. Owng to the smlarty between pasted regon and coped regon, descrptors are then matched between each other to seek for any possble forgery n mages. Fg. 3 shows some examples of copy-move forgery detecton results yelded by the method proposed by A. N. Myna et al. [27] and by the method proposed by H. Huang [28]. (d) (d) (e) (f) Fg. 2. Image tamper detecton and recovery. orgnal mage;. tampered mage;. result of temper detecton; (d). recovered mage. Copy-move forgery s a specfc type of mage tamperng, where a part of the mage s coped and pasted. Many methods have been proposed to detect copy-move forgeres [23][24][25][26][27][28]. G. L (g) (h) () Fg. 3., &. orgnal mages; (d), (e), & (f). detected results by A. N. Myna et al.; (g), (h), & (). detected results by H. Huang et al.. ISSN: Issue 5, Volume 5, May 2009

3 A good copy-move forgery detector should be robust to some types of manpulatons ncludng lossy compresson, Gaussan nose, and rotaton or scalng. Most of the exstng methods do not deal wth all those manpulatons and are tme-consumng. In ths paper, we focus on detecton of the copy-move forgery, and propose an effcent method for detectng copy-move forgeres n dgtal mages, whch s robust to some types of manpulatons ncludng lossy compresson, Gaussan nose, and rotaton. To mprove the computatonal complexty n detectng the regons of forgeres, we propose to use the radx sort for sortng the feature vectors of the dvded sub-blocks, as an alternatve to lexcographc sortng, whch s commonly used by the exstng copy-move forgery detecton schemes. Our expermental results show that the proposed method can detect copy-move forgeres n the mages very accurately, even when the coped regon was undergone severe mage manpulatons, such as addtve Gaussan nose, lossy JPEG compresson, and rotaton etc, or even compound processng. In addton, t s observed that use of radx sort consderably mproves the tme effcency at the expense of a slght reducton n the robustness. The rest of ths paper s organzed as follows. Related work s dscussed n Secton 2. In Secton 3, the proposed method s descrbed n detals. In Sectons 4 and 5, we show some expermental results and make a concluson for ths paper. respectvely. If we let v(x) denote the feature vector of a block X, then v(a 1 ) = v(b 1 ), v(a 2 ) = v(b 2 ), and v(a 3 ) = v(b 3 ). When the feature vectors are sorted, dentcal vectors would be grouped together n the sorted lst, as shown n Fg. 4, from whch the duplcated regons could be easly detected. The tme requred by sortng the feature vectors s dependent on both the total number of dvded blocks and the sze of feature vectors. 2 Related Work In most methods of copy-move forgery detecton, the detected mage s dvded nto overlappng blocks of equal sze, whch are represented n the form of (feature) vectors, and then lexcographcally sorted for later detecton. Suppose a detected mage of sze N N s dvded nto k = (N-b+1) 2 overlappng blocks of sze b b, represented as vectors of length b 2. In the sorted lst, vectors correspondng to blocks of smlar content would be close to each other n the lst, and thus dentcal regons could be easly detected by evaluatng shft vectors formed by pars of adjacent feature vectors, and detectng large accumulated numbers of shft vectors. The mage shown n Fg. 4 s a tampered result, where the regons enclosed by dotted rectangles are duplcated, of the orgnal mage gven n Fg. 4 by ncurrng copy-move forgery. Dvdng the tempered mage nto overlappng blocks for forgery detecton, we can observe that, for example, blocks B 1, B 2, and B 3 are copes of blocks A 1, A 2, and A 3, Fg. 4.. An orgnal mage;. Three pars of dentcal blocks are enclosed by blue squares;. Sorted lst of feature vectors, n whch dentcal vectors. ISSN: Issue 5, Volume 5, May 2009

4 A. C. Popescu et al. [24] used the prncple component analyss (PCA) and represented each block of sze as a feature vector of length 32, and lexcographcally sorted the vectors n O(32 k lgk) tme. The tme complexty for sortng was reduced by G. L et al. [25] to O(8k lgk) by the use of SVD. W. Luo et al. [26] defned a feature vector of 7-dmenson to represent blocks so as the tme complexty for sortng was further reduced to O(7k lgk). In ths paper, we shall propose a further effcent method for sortng the feature vectors, whose tme complexty s reduced to O(9k ). f x f = Ave ( B) = Ave ( S 1 ) /(4 Ave ( B) + ε 1 ) f = Ave ( S 5 ) Ave ( B) = where f 255 f f m m 1 m 2 + ε 2 m 1 = max 6 9 f = 1, { f } and m = mn { f }. 2 f 2 5, f 6 9. f = 1, f 2 5, f 6 9, 6 9 (1) (2 ) 3 The Proposed Method For resstng aganst varous modfcatons and mprovng the effcency for sortng feature vectors, we represent each block B of sze b b ( = ) by a 9-dmensonal feature vector v B = (x 1, x 2,, x 9 ), whch s defned as follows. Frstly, the block B s dvded nto four equal-szed sub-blocks, S 1, S 2, S 3, and S 4, as shown n Fg. 5 and let Ave(.) denote the average ntensty functon. Then as descrbed n (1), f 1 denotes the average ntensty of the block B, the entres f 2, f 3, f 4, and f 5 denote the ratos of the average ntenstes of the blocks S 1, S 2, S 3, and S 4 to f 1, respectvely, and f 6, f 7, f 8, and f 9 stand for the dfferences of the average ntenstes of the blocks S 1, S 2, S 3, and S 4 from f 1, respectvely. Fnally, entres f s are normalzed to ntegers x s rangng from 0 to 255, as descrbed n (2), where denotes a floor operator. Although these 9 enttes contan duplcated nformaton, they together possess hgher capablty of resstance aganst some modfcatons, such as JPEG compresson and Gaussan nose. Unlke the matrx constructed by A. C. Popescu et al. [24], whch stores floatng numbers, the feature vectors we extract store ntegers. As a result, we may use the effcent radx sort algorthm to perform lexcographcal sortng over those vectors. If the gven mage of sze N N s dvded nto overlappng blocks of sze b b, then there are 2 totally k blocks, where k = ( N b +1). Let v 1, v 2,,v k be the feature vectors correspondng to these k blocks. To perform radx sort on these vectors of sze 9, we regard each of them as a 9-dgt number wth each dgt rangng from 0 to 255. The sortng algorthm s gven n the followng, where the nput array A stores these vectors; that s, A[] = v, 1 k, and d = 9. RADIX-SORT(A,d) for j 1 to d do use a stable sort to sort array A on dgt j Block B Fg. 5. A block B s dvded nto four equal-szed sub-blocks S 1, S 2, S 3, and S 4. Snce each dgt n the vectors, rangng from 0 to 255, s not large, countng sort s chosen as the stable sort used n the radx sort. Each pass over k numbers then takes tme O(256+k). There are 9 passes, so the total tme for sortng the feature vectors s O(9(256+k)) = O(9k) snce 256 << k. From what follow, we let v 1, v 2,,v k denote sorted lst of the feature vectors of blocks B 1, B 2,, B k, respectvely. The poston of the top-left corner pont of each block B s recorded n P(B ) and a shft vector s defned as the dfference of two adjacent feature vectors n the sorted lst as shown n (3).Two duplcated regons caused by copy-move forgery form a number of pars of dentcal feature vectors, each par then make the same shft vector, thus the ISSN: Issue 5, Volume 5, May 2009

5 accumulatve number of a shft vector can be used to detect the duplcated regons. u( ) = P( B + 1) P( B ) (3) As the example llustrated n Fg. 6, several pars of correspondng feature vector make same vector u, whose accumulatve number s 7 n ths example. Wth those accumulatve numbers of shft vectors, we detect the duplcated regons as follows. For the accumulatve number of a shft vector greater than a gven threshold T 1, the four corner ponts of all the correspondng blocks are marked. For example, f the accumulated number of a shft vector u 0 s greater than T 1, then for each, the top-left ponts of the respectve blocks B and B +1 correspondng to v and v +1 are marked f u() = u 0. Fg. 7 shows the result of marked ponts for Fg. 4. Fnally, the medum flterng s performed to remove noses and the connected component analyss s appled to obtan the fnal detected result as gven n Fg. 7. Fg. 7.. Corner ponts of detected blocks are marked accordng to the accumulated numbers of shft vectors for the tampered mage gven n Fg. 4;. fnal detected result. Fg. 6. Duplcated regons form several dentcal shft vector u. To deal wth rotaton, we smply detect on the gven mage assocated wth ts rotated versons. In our experments, we consdered rotatons through angles of 90, 180, and 270 degrees. Ths way we may detect rotated copy-move forgeres wth any angle of rotaton. As shown n Fg. 8, the regon s coped, rotated by angle 90 degrees, and pasted to another regon n the mage. In ths case, the accumulated number of shft vectors cannot reflect the duplcaton. To detect rotated coped mages, we combne three rotated versons of the mage wth the orgnal one, and perform forgery detecton on ths combned mage. Fg. 8. A regon s coped, rotated through 90 degrees, and pasted to another regon. 4 The Expermental Results The proposed method was mplemented on a computer of CPU 3.0GHz wth memory 1GB. The ISSN: Issue 5, Volume 5, May 2009

6 test mages were cropped from 50 natural mages. We tested over 50 tampered mages wth no modfcaton, 150 tampered mages wth JPEG compresson, and 150 tampered mages wth Gaussan nose, 50 tampered mages wth rotaton mages, 150 tampered mages wth rotaton and Gaussan nose, and 150 tampered mages wth rotaton and JPEG compresson. For detectng on color mages, only the green channel s used snce the human eyes are most senstve to the green color. For parameter settng, we set b = 16, T 1 = 100, and T 2 = 10. More detected results over tampered mages are shown n Fg.s 9~11. Fg. 12 shows the detected results over compressed tampered mages wth varous qualty factors. Fg. 13 shows the detected results over some mages wth Gaussan nose at varous SNRs (sgnal to nose ratos). Fg. 14 shows the detected results over some mages wth rotaton. Table 1 shows detecton rates for some datasets of copy-move mages wth some modfcaton. Table 2 detecton rates for some datasets of copy-move mages wth rotaton and some other modfcaton. Fg. 9.. The orgnal mages;. the tampered mages;. the detectng results. Fg The orgnal mages;. the tampered mages;. the detectng results. ISSN: Issue 5, Volume 5, May 2009

7 Fg. 12. Detected results over compressed versons of the mage gven n Fg. (4a), wth varous qualty factors (QFs):. QF = 90;. QF = 70;. QF = 50. Fg The orgnal mages;. the tampered mages;. the detectng results. Fg. 13. Detected results for the mage gven n Fg. (4a) wth Gaussan nose at varous SNRs:. SNR = 10db;. SNR = 20db;. SNR = 35db. ISSN: Issue 5, Volume 5, May 2009

8 Table 1. Detecton rates for datasets of copy-move wth/wthout modfcaton. Data sets of Copy-move mages wthout mdfcaton JPEGcompresson QF = 100 JPEG compresson QF = 90 JPEG compresson QF = 80 Gaussan nose SNR = 10 Gaussan nose SNR = 20 Gaussan nose SNR = 35 No. of Detecton mages rate (%) be detected, our method does not deal wth rotaton arbtrary angles. In the future, we would lke to search for some feature nvarant to rotaton to deal wth ths problem. In addton to rotaton problem, we would try to extend our work to vdeo mages. Table 2. Detecton rates for datasets of copy-move mages wth rotaton and some other modfcaton. Data sets of Copy-move mages wth rotaton wthout mdfcaton JPEGcompresson QF = 100 JPEG compresson QF = 90 JPEG compresson QF = 80 Gaussan nose SNR = 10 Gaussan nose SNR = 20 Gaussan nose SNR = 35 No. of mages Detecton rate (%) Concluson and Future Work In ths paper, we propose an effcent method for copy-move forgery detecton. Usng of radx sort dramatcally mproves the tme complexty and the adopted features enhance the capablty of resstng of varous attacks such as JPEG compresson and Gaussan nose. Both effcency and hgh detecton rates have been demonstrated n our expermental results. However, a few small coped regons were not successfully detected. Although duplcated regons wth rotaton through some fxed angles can (d) Fg. 14. Detectng results for the rotated duplcated regons. ISSN: Issue 5, Volume 5, May 2009

9 Acknowledgements The work presented n ths paper was supported by the Natonal Scence Councl, grant NSC E References: [1] C. T. Hseh and Y. K. Wu, Geometrc Invarant Sem-fragle Image Watermarkng Usng Real Symmetrc Matrx, WSEAS Transacton on Sgnal Processng, Vol. 2, Issue 5, May 2006, pp [2] C. T. Hseh, Y. K. Wu, and K. M. Hung, An Adaptve Image Watermarkng System Usng Complementary Quantzaton, WSEAS Transacton on Informaton Scence and Applcatons, Vol. 3, Issue 12, 2006, pp [3] K. M. Hung, C. T. Hseh and Y. K. Wu, Mult-Purpose Watermarkng Schemes for Color Halftone Image Based on Wavelet and Zernke Transform, WSEAS Transacton on Computer, Vol. 6, Issue 1, 2007, pp [4] F. Hartung and M. Kutter, Multmeda Watermarkng Technques, n Proceedngs of the IEEE, Vol. 87, No. 7, July 1999, pp [5] P. Meerwald and A. Uhl, A Survey of Wavelet-Doman Watermarkng Algorthms, n Proceedubgs of SPIE, Electronc Imagng, Securty and Watermarkng of Multmeda Contents, Vol. 4314, 2001, pp [6] W. Lu, F. L. Chung, and H. Lu, Blnd Fake Image Detecton Scheme Usng SVD, IEICE Transacton on Communcatons, Vol. E89-B, No. 5, May 2006, pp [7] M. S. Wang and W. C. Chen, A Majorty-Votng based Watermarkng Scheme for Color Image Tamper Detecton and Recovery, Computer Standards & Interfaces, Vol. 29, Issue 5, 2007, pp [8] P. L. Ln, C. K. Hseh, and P. W. Huang, A Herarchcal Dgtal Watermarkng Method for Image Tamper Detecton and Recovery, Pattern Recognton, Vol. 38, Issue 12, 2005, pp [9] K. F. L, T. S. Chen, and S. C. Wu, Image Tamper Detecton and Recovery System Based on Dscrete Wavelet Transformaton, Internatonal Conference Communcatons, Computers and Sgnal Processng, Vol. 1, 2001, pp [10] A. C. Popescu and H. Fard, Exposng Dgtal Forgeres by Detectng Traces of Resamplng, IEEE Transactons on Sgnal Processng, Vol. 53, 2005, pp [11] E. S. Gop, N. Lakshmanan, T. Gokul, S. KumaraGanesh, and P. R. Shah, Dgtal Image Forgery Detecton usng Artfcal Neural Network and Auto Regressve Coeffcents, Electrcal and Computer Engneerng, 2006, pp [12] M. K. Johnson and H. Fard, Exposng Dgtal Forgeres by Detectng Inconsstences n Lghtng, n Proceedngs of ACM Multmeda and Securty Workshop, New York, 2005, pp [13] R. Brunell, Estmaton of Pose and Illumnant Drecton for Face Processng, Image and Vson Computng, Vol. 15, No. 10, October 1997, pp [14] A. P. Pentland, Fndng the llumnant drecton, Journal of the Optcal Socety of Amerca, Vol. 72, Issue 4, 1982, pp [15] W. Zhou and C. Kambhamettu, Estmaton of Illumnant Drecton and Intensty of Multple Lght Sources, n Proceedngs of the 7th European Conference on Computer Vson-Part IV, 2002, pp [16] P. Nllus and J. O. Eklundh, Automatc Estmaton of the Projected Lght Source Drecton, n Proceedngs of 2001 IEEE Computer Socety Conference on Computer Vson and Pattern Recognton, Vol. 1, 2001, pp [17] T. E. Boult and G. Wolberg, Correctng Chromatc Aberratons Usng Image Warpng, n Proceedngs of Computer Vson and Pattern Recognton, 1992, pp [18] M. K. Johnson and H. Fard, Exposng Dgtal Forgeres Through Chromatc Aberraton, n Proceedngs of the 8th workshop on Multmeda and securty, 2006, pp [19] J. Lukas, J. Frdch, and M. Goljan, Detectng Dgtal Image Forgeres Usng Sensor Pattern Nose, n Proceedngs of the SPIE Conference on Securty, Steganography, and Watermarkng of Multmeda Contents, Vol. 6072, January 2006, pp [20] N. Khanna, A. K. Mkklnen, G. T. C. Chu, J. P. Allebach, and E. J. Delp, Scanner Identfcaton Usng Sensor Pattern Nose, n Proceedngs of the SPIE Internatonal Conference on Securty, Steganography, and Watermarkng of Multmeda Contents IX, Vol. 6505, No. 1, 2007, pp K. [21] J. Lukas, J. Frdrch, and M. Goljan, Determnng Dgtal Image Orgn Usng Sensor Imperfectons, n Proceedngs of SPIE Electronc Imagng, Image and Vdeo ISSN: Issue 5, Volume 5, May 2009

10 Communcaton and Processng, January 16-20, 2005, pp [22] A. C. Popescu and H. Fard, Exposng Dgtal Forgeres n Color Flter Array Interpolated Images, IEEE Transactons on Sgnal Processng, Vol. 53, 2005, pp [23] J. Frdrch, D. Soukal, and J. Lukas, Detecton of Copy-Move Forgery n Dgtal Images, n Proceedngs of Dgtal Forensc Research Workshop, August [24] A. C. Popescu and H. Fard, Exposng Dgtal Forgeres by Detectng Duplcated Image Regons, Techncal Report, TR , Department of Computer Scence, Dartmouth College, [25] G. L, Q. Wu, D. Tu, and S. Sun, A Sorted Neghborhood Approach for Detectng Duplcated Regons n Image Forgeres based on DWT and SVD, n Proceedngs of IEEE Internatonal Conference on Multmeda and Expo, Bejng Chna, July 2-5, 2007, pp [26] W. Luo, J. Huang, and G. Qu, Robust Detecton of Regon Duplcaton Forgery n Dgtal Image, n Proceedngs of the 18th Internatonal Conference on Pattern Recognton, Vol. 4, 2006, pp [27] A. N. Myna, M. G. Venkateshmurthy, and C. G. Patl, Detecton of Regon Duplcaton Forgery n Dgtal Images Usng Wavelets and Log-Polar Mappng, n Proceedngs of the Internatonal Conference on Computatonal Intellgence and Multmeda Applcatons (ICCIMA 2007), Vol. 3, 2007, pp [28] H. Huang, W. Guo, and Y. Zhang, Detecton of Copy-Move Forgery n Dgtal Images Usng SIFT Algorthm, n Proceedngs of IEEE Pacfc-Asa Workshop on Computatonal Intellgence and Industral Applcaton, Vol. 2, 2008, pp ISSN: Issue 5, Volume 5, May 2009