E-1E-9 1 5) 15) ) 1 E-1 E-5 E-1E-5 11) 14 (E-2)(E-3) E-1E-5 Exif E-6E-9 E-6 2.1, 2.2 E E-1 E-5 E-6E-9 1-1) 1 Exif 11) (E-1) (E-2E-5) E-7
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1 J. Struct. Constr. Eng., AIJ, Vol. 77 No. 674, , Apr., 2012 Satoshi FUJIMOTO Construction photo-editing has prevailed among the Japanese construction industries as digitalization of photography becomes popular. Some of these photo-editing are made on as-needed basis in construction management process, while some are found to be the forgery of construction record. In this paper, categorization of photo-editing methods are firstly made based on the case survey in construction industries. Secondly, technical requirements of construction photo-editing detection method is summarized with particular consideration for editing needs in construction management processes. Thirdly, we have proposed a newly developed photo-editing detection method called OI expected-noise method, and examined the performance using simulated samples of edited construction photography. The method has shown high detection performance and fulfilled the above mentioned requirements. Keywords : Construction photography, photo-editing, Detection of Forgery, Construction management, Construction record, Information technology 1) 2) 3) ) () 5) ) 2) 1)OI 3) 2) 1) Assist. Prof., Graduate School of Eng., Social Environment and Space Div., Hiroshima Univ., Dr. Eng. 647
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4 OI N 1 N i OIX OIY ) (6b) var var ( ioi OI 1 OIX OIY 1 OI x max OI xmax OI OI x' xmin xmin P ) P 2 (6c) ) (6)denoise 36,37) Non-Local-Means 38) 1 PC web 2 (6a) 1 OIX, OIY OI (6) -1 Exif ( 1 E-1 2 (ex ) 25) C % (6a) N 10 P-1 10 P-2 (, 10 () P-3 10 P-4 ( 10 1 P-5 24 P-6 29 P-7 32 P-8 26 P
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10 1) :,2011.3, houhi.html, ( ) (): (, , 22.html, ( ) 3),,, :,, pp.55-65, Vol.11, ) :, pp.26-27, , kensetsu-plaza.com/backnumber/details.php?no=24 ( ) : (), 18 1, ) :, pp.6-7, , 7) : p.8, ) :, pp.2-3, ) NEXCO, NEXCO, NEXCO : ver.2.00, (), p.10, ) () : (),p.1, ) ():, nfodb/software_list/hikakuhyou/gazou.pdf, ( ) (JEITA): Exchangeable image file format for digital still cameras : Exif Unified Version 2.21 (CP-3451-B), ) :, JACIC , douga/071114morikawa0103/contents/index.html ( ) 14) IT :, , setsu-plaza.com/cad/special_topic/2009/04/topic02.html ( ) C. ey, J-L.Dugelay: A Survey of Watermarking Algorithms For Image Authentication, EUASIP Journal on Applied Signal Processing, pp , Vol.2002, Issue 1, ), 2, ),, :,, pp ) IT 2,, pp.74-75, ),, pp.86-87, ), :,, pp.30-46, ),, p.45, ) : () 22, ( ) T.V. Lanh, K.S. Chong, S. Emmanuel, M.S. Kankanhalli: A Survey on Digital Camera Image Forensic Method, pp16-19, IEEE International Conference on Multimedia and Expo, ) K.S. Choi, E.Y. Lam, K.K.Y. Wong: Source Camera Identification Using Footprints from Lens Aberration, Proc. of SPIE-IS&T Electronic Imaging, pp.60690j j-8, SPIE Vol.6069, ) J. Lucáš, J. Fridrich and M. Goljan: Detecting Digital Image Forgeries Using Sensor Pattern Noise, Proc. of Society of Photo- Optical Instrumentation Engineers Conference, Vol. 6072, pp , ) Z.J. Geradts, J. Bijhold, M. Kieft, K. Kurosawa, K. Kuroki, N. Saitoh, Methods for Identification of Images Acquired with Digital Cameras, Proc. of SPIE, Enabling Technologies for Law Enforcement and Security, vol.4232, ) A.C. Popescu, H. Farid : Exposing Digital Forgeries in Color Filter Array Interpolated Images, IEEE Transactions on Signal Processing, pp , Vol.53, No.10, ) S. Bayram, H. T. Sencar, N. Memon, and I. Avcibas: Source Camera Identification Based on CFA Interpolation, pp.69-72, IEEE International Conference on Image Processing, ) M.K. Johnson, H. Farid: Exposing Digital Forgeries by Detecting Inconsistencies in Lighting, Proc. of the 7th Workshop on Multimedia and Security, pp.1-10, ) S. Ye, Q. Sun, E.C. Chang: Detecting Digital Image Forgeries by Measuring Inconsistencies of Blocking Artifact, IEEE International Conference on Multimedia and Expo, pp.12-15, ) B. Mahdian, S. Saic: Using noise inconsistencies for blind image forensics, Image and Vision Computing, pp , Vol. 27, Issue 10, ) G. E. Healey,. Kondepudy: adiometric CCD Camera Calibration and Noise Estimation, IEEE Transaction on Pattern ecognition and Machine Intelligence, pp , Vol.16, No.3, ) Y.Tsin, V. amesh, T. Kanade: Statistical Calibration of CCD Imaging Process, 8th International Conference on Computer Vision (ICCV'01), pp , Vol.1, ). Widenhorn, M.M. Blouke, A. Weber, A. est, E. Bodegom : Temperature dependence of dark current in a CCD, Proc. Of SPIE, pp , Vol.2669, ) A.V. Oppenheim,.W. Schafer: Digital signal processing, Prentice Hall International Editions, ) M.C. M. Mukesh C. Gadiya.C. Motwani,F. C. Harris Jr.: Survey of Image Denoising Techniques, Proc. of Global Signal Processing Expo and Conference(GSPx), pp27-30, ) A. Buades, B. Coll, J.M. Morel: A review of image denoising methods, with a new one, Multiscale Modeling and Simulation, pp , Vol.4, No.2, ) A. Buades, B. Coll, J.M. Morel, A Non-Local Algorithm for Image Denoising, IEEE Computer Society Conference on Computer Vision and Pattern ecognition (CVP'05), pp.60-65, Vol.2, ) D.F. Bauer: Constructing confidence sets using rank statistics, Journal of the American Statistical Association, Vol.67, No.339, pp , ) JPEGclub.org: jpegtran, ( ),, pp , ) N 33 N 32 denoise 2) 2 32 (1) f 33 3)
(a) 1 (b) 3. Gilbert Pernicka[2] Treibitz Schechner[3] Narasimhan [4] Kim [5] Nayar [6] [7][8][9] 2. X X X [10] [11] L L t L s L = L t + L s
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4. C i k = 2 k-means C 1 i, C 2 i 5. C i x i p [ f(θ i ; x) = (2π) p 2 Vi 1 2 exp (x µ ] i) t V 1 i (x µ i ) 2 BIC BIC = 2 log L( ˆθ i ; x i C i ) + q
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4 4 2 RAW 4 4 4 (PCA) 4 4 4 4 RAW RAW [5] 4 RAW 4 Park [12] Park 2 RAW RAW 2 RAW y = Mx + n. (1) y RAW x RGB M CFA n.. R G B σr 2, σ2 G, σ2 B D n ( )
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RAW 4 E-mail: hakiyama@ok.ctrl.titech.ac.jp Abstract RAW RAW RAW RAW RAW 4 RAW RAW RAW 1 (CFA) CFA Bayer CFA [1] RAW CFA 1 2 [2, 3, 4, 5]. RAW RAW RAW RAW 3 [2, 3, 4, 5] (AWGN) [13, 14] RAW 2 RAW RAW RAW
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