{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T16:51:02Z","timestamp":1792515062927,"version":"4.5.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"3-4","license":[{"start":{"date-parts":[[2019,12,4]],"date-time":"2019-12-04T00:00:00Z","timestamp":1575417600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,12,4]],"date-time":"2019-12-04T00:00:00Z","timestamp":1575417600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2020,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In this paper we deal with the problem of digital camera identification by photographs. Identifying camera is possible by analyzing camera\u2019s sensor artifacts that occur during the process of photo processing. The problem of digital camera identification has been popular for a long time. Recently many effective and robust algorithms for solving this problem have been proposed. However, almost all solutions are based on state-of-the-art algorithm, proposed by Luk\u00e1s et al. in 2006. Core of this algorithm is to calculate the so-called sensor pattern noise based on denoising images with wavelet-based denoising filter. Such technique is very efficient, but very time consuming. In this paper we consider tracing cameras by analyzing defects of their optical systems, like vignetting and lens distortion. We show that analysis of vignetting defect allows for recognizing brand of the camera. Lens distortion can be used to distinguish images from different cameras. Experimental evaluation was carried out on 60 devices (compact cameras and smartphones) for a total number of 12 051 images, with support of the Dresden Image Database. Proposed methods do not require denoising images with wavelet-based denoising filter what has a significant influence for speed of image processing, compared with state-of-the-art algorithm.<\/jats:p>","DOI":"10.1007\/s11042-019-08182-z","type":"journal-article","created":{"date-parts":[[2019,12,4]],"date-time":"2019-12-04T10:02:47Z","timestamp":1575453767000},"page":"2945-2963","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Digital camera identification based on analysis of optical defects"],"prefix":"10.1007","volume":"79","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4488-3488","authenticated-orcid":false,"given":"Jaroslaw","family":"Bernacki","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,12,4]]},"reference":[{"key":"8182_CR1","unstructured":"Baar T, van Houten W, Geradts Z (2012) Camera identification by grouping images from database, based on shared noise patterns. CoRR abs\/1207.2641"},{"key":"8182_CR2","doi-asserted-by":"crossref","unstructured":"Bernacki J, Klonowski M, Syga P (2017) Some Remarks about tracing digital cameras \u2013 faster method and usable countermeasure. In: Proceedings of the 14th International Joint Conference on e-Business and Telecommunications (ICETE 2017), pp 343\u2013350","DOI":"10.5220\/0006434803430350"},{"key":"8182_CR3","first-page":"30","volume":"3","author":"GJ Bloy","year":"2008","unstructured":"Bloy GJ (2008) Blind camera fingerprinting and image clustering. IEEE Trans Pattern Anal Mach Intell 3:30","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8182_CR4","doi-asserted-by":"crossref","unstructured":"Chapman GH, Thomas R, Thomas R, Koren Z, Koren I (2015) Enhanced correction methods for high density hot pixel defects in digital imagers","DOI":"10.1117\/12.2083147"},{"key":"8182_CR5","unstructured":"Claus D, Fitzgibbon AW (2005) A rational function lens distortion model for general cameras. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905)"},{"issue":"18","key":"8182_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2352\/ISSN.2470-1173.2016.18.DPMI-249","volume":"2016","author":"Varuna De Silva","year":"2016","unstructured":"De Silva V, Chesnokov V, Larkin D (2016) A Novel Adaptive Shading Correction Algorithm for Camera Systems, Electronic Imaging, Digital Photography and Mobile Imaging XII, pp 1\u20135(5)","journal-title":"Electronic Imaging"},{"key":"8182_CR7","doi-asserted-by":"publisher","unstructured":"de Villiers JP, Leuschner FW, Geldenhuysm R (2008) Centi-pixel accurate real-time inverse distortion correction, 2008 International Symposium on Optomechatronic Technologies. SPIE. https:\/\/doi.org\/10.1117\/12.804771","DOI":"10.1117\/12.804771"},{"key":"8182_CR8","doi-asserted-by":"crossref","unstructured":"Deng Z, Gijsenij A, Zhang J (2011) Source camera identification using auto-white balance approximation. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp 57\u201364","DOI":"10.1109\/ICCV.2011.6126225"},{"key":"8182_CR9","unstructured":"(2008) Dresden Image Database. http:\/\/forensics.inf.tu-dresden.de\/ddimgdb\/ Online; accessed 3 December 2018"},{"key":"8182_CR10","unstructured":"Fuentes L, et al. (2015) Revisiting Image Vignetting Correction by Constrained Minimization of Log-Intensity Entropy Volume 9095 of the book series Lecture Notes in Computer Science (LNCS)"},{"key":"8182_CR11","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.patrec.2015.09.009","volume":"82","author":"C Galdi","year":"2016","unstructured":"Galdi C, Nappi M, Dugelay J (2016) Multimodal authentication on smartphones: combining iris and sensor recognition for a double check of user identity. Pattern Recogn Lett 82:144\u2013153","journal-title":"Pattern Recogn Lett"},{"key":"8182_CR12","doi-asserted-by":"crossref","unstructured":"Gloe T (2012) Feature-based forensic camera model identification. LNCS Transactions on Data Hiding and Multimedia Security (DHMMS), to appear","DOI":"10.1007\/978-3-642-31971-6_3"},{"key":"8182_CR13","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1007\/978-3-642-04438-0_38","volume-title":"Digital Watermarking","author":"Miroslav Goljan","year":"2009","unstructured":"Goljan M (2008) Digital camera identification from images - estimating false acceptance probability. In: Digital Watermarking, 7th International Workshop, IWDW 2008, pp 454\u2013468"},{"key":"8182_CR14","doi-asserted-by":"crossref","unstructured":"Goljan M, Fridrich J (2014) Estimation of lens distortion correction from single images. In: Proceedings volume 9028, media watermarking, Security, and Forensics 2014; 90280N","DOI":"10.1117\/12.2036804"},{"key":"8182_CR15","doi-asserted-by":"publisher","unstructured":"Goljan M, Fridrich J, Filler T (2009) Large scale test of sensor fingerprint camera identification, In: Proceedings of SPIE - The International Society for Optical Engineering, https:\/\/doi.org\/10.1117\/12.805701","DOI":"10.1117\/12.805701"},{"key":"8182_CR16","doi-asserted-by":"publisher","unstructured":"Goljan M, Fridrich J, Filler T (2010) Managing a Large Database of Camera Fingerprints, In: Proceedings of SPIE - The International Society for Optical Engineering, https:\/\/doi.org\/10.1117\/12.838378","DOI":"10.1117\/12.838378"},{"issue":"1","key":"8182_CR17","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1109\/TIFS.2010.2099220","volume":"6","author":"M Goljan","year":"2011","unstructured":"Goljan M, Fridrich JJ, Chen M (2011) Defending against fingerprint-copy attack in sensor-based camera identification. IEEE Trans Inf Forensics Secur 6(1):227\u2013236","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"8182_CR18","doi-asserted-by":"crossref","unstructured":"Hu Y, Li C-T, Lai Z, Zhang S Fast camera fingerprint search algorithm for source camera identification. In: Proceedings of the 5th International Symposium on Communications, Control and Signal Processing, ISCCSP 2012, Rome, Italy, 2-4 May 2012","DOI":"10.1109\/ISCCSP.2012.6217842"},{"key":"8182_CR19","unstructured":"Hugin Photo Stitcher http:\/\/hugin.sourceforge.net\/, last access 4 December 2018"},{"key":"8182_CR20","first-page":"3","volume-title":"Image Noise and Digital Image Forensics","author":"T Julliand","year":"2016","unstructured":"Julliand T, Nozick V, Talbot H (2016) Image Noise and Digital Image Forensics. Springer International Publishing, Cham, pp 3\u201317"},{"issue":"2","key":"8182_CR21","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1109\/TIFS.2011.2168214","volume":"7","author":"X Kang","year":"2012","unstructured":"Kang X, Li Y, Qu Z, Huang J (2012) Enhancing source camera identification performance with a camera reference phase sensor pattern noise. IEEE Trans Inf Forensics Secur 7(2):393\u2013402","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"8182_CR22","doi-asserted-by":"crossref","unstructured":"Khan MB, Nisar H, Ng CA, Lo PK (2016) A vignetting correction algorithm for Bright-Field microscopic images of activated sludge. In: 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","DOI":"10.1109\/DICTA.2016.7796999"},{"key":"8182_CR23","doi-asserted-by":"crossref","unstructured":"Kordecki A, Bal A, Palus H (2016) Local polynomial model: a new approach to vignetting correction. In: Proc SPIE 10341 Ninth International Conference on Machine Vision","DOI":"10.1117\/12.2268566"},{"key":"8182_CR24","doi-asserted-by":"crossref","unstructured":"Kordecki A, Bal A, Palus H (2016) A study of vignetting correction methods in camera colorimetric calibration. In: Proc SPIE 10341 9th International Conference on Machine Vision","DOI":"10.1117\/12.2268532"},{"issue":"8","key":"8182_CR25","first-page":"1417","volume":"10","author":"A Kordecki","year":"2016","unstructured":"Kordecki A, Palus H, Bal A (2016) . Practical vignetting correction method for digital camera with measurement of surface luminance distribution 10(8):1417\u20131424","journal-title":"Practical vignetting correction method for digital camera with measurement of surface luminance distribution"},{"key":"8182_CR26","unstructured":"Lanh TV, Chong K, Emmanuel S, Kankanhalli MS (2007) A survey on digital camera image forensic methods. In: Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME, 2007, pp 16\u201319"},{"key":"8182_CR27","unstructured":"Lee SY, Cho HJ, Lee HJ (2016) Method for vignetting correction of image and apparatus therefor US 20160189353 A1"},{"key":"8182_CR28","unstructured":"Li H, Hartley R (2005) A Non-iterative Method for Correcting Lens Distortion from Nine Point Correspondences, In: Proc OmniVision\u201905, ICCV-workshop"},{"issue":"4","key":"8182_CR29","doi-asserted-by":"publisher","first-page":"768","DOI":"10.1109\/LGRS.2013.2278626","volume":"11","author":"X Li","year":"2014","unstructured":"Li X, Shen H, Zhang L, Zhang H, Yuan Q (2014) Dead pixel completion of aqua MODIS band 6 using a robust m-estimator multiregression. IEEE Geosci Remote Sensing Lett 11(4):768\u2013772","journal-title":"IEEE Geosci Remote Sensing Lett"},{"key":"8182_CR30","unstructured":"Lukas J, Fridrich JJ, Goljan M (2016) Matlab implementation"},{"issue":"2","key":"8182_CR31","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1109\/TIFS.2006.873602","volume":"1","author":"J Luk\u00e1s","year":"2006","unstructured":"Luk\u00e1s J, Fridrich JJ, Goljan M (2006) Digital camera identification from sensor pattern noise. IEEE Trans Inf Forensics Secur 1(2):205\u2013214","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"8182_CR32","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.1016\/S0031-3203(00)00068-6","volume":"34","author":"S-W Park","year":"2001","unstructured":"Park S-W, Hong K-S (2001) Practical ways to calculate camera lens distortion for real-time camera calibration. Pattern Recogn 34:1199\u20131206","journal-title":"Pattern Recogn"},{"key":"8182_CR33","doi-asserted-by":"publisher","first-page":"78038","DOI":"10.1109\/ACCESS.2018.2884710","volume":"6","author":"Tong Qiao","year":"2018","unstructured":"Qiao T, Retraint F (2018) Identifying individual camera device from RAW images, digital object identifier, vol 6, pp 78038-78054 IEEE Access, https:\/\/doi.org\/10.1109\/ACCESS.2018.2884710","journal-title":"IEEE Access"},{"key":"8182_CR34","unstructured":"Ray SF (2002) Applied photographic optics, 3rd ed. Focal Press ISBN 978-0-240-51540-3"},{"key":"8182_CR35","doi-asserted-by":"crossref","unstructured":"Schwarting M, Burton T, Yampolskiy R (2016) On the obfuscation of image sensor fingerprints, 2015 annual global online conference on information and computer technology IEEE","DOI":"10.1109\/GOCICT.2015.21"},{"key":"8182_CR36","doi-asserted-by":"crossref","unstructured":"Taspinar S, Mohanty M, Memon ND (2016) PRNU Based source attribution with a collection of seam-carved images. In: 2016 IEEE International Conference on Image Processing, ICIP, pp 156\u2013160, vol 2016","DOI":"10.1109\/ICIP.2016.7532338"},{"key":"8182_CR37","doi-asserted-by":"publisher","first-page":"12457","DOI":"10.1007\/s11042-016-3660-3","volume":"76","author":"H Yao","year":"2017","unstructured":"Yao H, Wang S, Zhang X, Qin C, Wang J (2017) Detecting image splicing based on noise level inconsistency. Multimed Tools Appl 76:12457\u201312479","journal-title":"Multimed Tools Appl"},{"key":"8182_CR38","doi-asserted-by":"publisher","first-page":"18139","DOI":"10.1007\/s11042-017-5206-8","volume":"77","author":"H Yao","year":"2018","unstructured":"Yao H, Cao F, Tang Z, Wang J, Qiao T (2018) Expose noise level inconsistency incorporating the inhomogeneity scoring strategy. Multimed Tools Appl 77:18139\u201318161","journal-title":"Multimed Tools Appl"},{"issue":"12","key":"8182_CR39","doi-asserted-by":"publisher","first-page":"2243","DOI":"10.1109\/TPAMI.2008.263","volume":"31","author":"Y Zheng","year":"2009","unstructured":"Zheng Y, Lin S, Kambhamettu C, Yu J, Kang SsB (2009) Single-Image Vignetting correction. IEEE Trans Pattern Anal Mach Intell 31(12):2243\u20132256","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"6","key":"8182_CR40","doi-asserted-by":"publisher","first-page":"1480","DOI":"10.1109\/TPAMI.2012.210","volume":"35","author":"Y Zheng","year":"2013","unstructured":"Zheng Y, Lin S, Kang SB, Xiao R, Gee JC, Kambhamettu C (2013) Single-Image Vignetting correction from gradient distribution symmetries. IEEE Trans Pattern Anal Mach Intell 35(6):1480\u20131494","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8182_CR41","doi-asserted-by":"crossref","unstructured":"Zeng H, Chen J, Kang X, Zeng W (2015) Removing camera fingerprint to disguise photograph source. In: 2015 IEEE International Conference on Image Processing, ICIP 2015, Quebec City, QC, Canada, September 27-30, 2015, IEEE, pp 1687\u20131691","DOI":"10.1109\/ICIP.2015.7351088"},{"issue":"7","key":"8182_CR42","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1109\/TCYB.2016.2557802","volume":"47","author":"H Zeng","year":"2017","unstructured":"Zeng H, Liu J, Yu J, Kang S (2017) A framework of camera source identification bayesian game. IEEE Transactions on Cybernetics 47(7):1757\u20131768","journal-title":"IEEE Transactions on Cybernetics"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-019-08182-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11042-019-08182-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-019-08182-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T16:54:53Z","timestamp":1665161693000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11042-019-08182-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,4]]},"references-count":42,"journal-issue":{"issue":"3-4","published-print":{"date-parts":[[2020,1]]}},"alternative-id":["8182"],"URL":"https:\/\/doi.org\/10.1007\/s11042-019-08182-z","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,4]]},"assertion":[{"value":"12 December 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2019","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 September 2019","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 December 2019","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}