Abstract
Feature-based camera model identification plays an important role in the toolbox for image source identification. It enables the forensic investigator to discover the probable source model employed to acquire an image under investigation. However, little is known about the performance on large sets of cameras that include multiple devices of the same model. Following the process of a forensic investigation, this paper tackles important questions for the application of feature-based camera model identification in real world scenarios. More than 9,000 images were acquired under controlled conditions using 44 digital cameras of 12 different models. This forms the basis for an in-depth analysis of a) intra-camera model similarity, b) the number of required devices and images for training the identification method, and c) the influence of camera settings. All experiments in this paper suggest: feature-based camera model identification works in practice and provides reliable results even if only one device for each camera model under investigation is available to the forensic investigator.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lyu, S., Farid, H.: How realistic is photorealistic? IEEE Transactions on Signal Processing 53(2), 845–850 (2005)
Dehnie, S., Sencar, H.T., Memon, N.: Digital image forensics for identifying computer generated and digital camera images. In: Proceedings of the 2006 IEEE International Conference on Image Processing (ICIP 2006), pp. 2313–2316 (2006)
Khanna, N., Mikkikineni, A.K., Chiu, G.T.C., Allebach, J.P., Delp, E.J.: Forensic classification of imaging sensor types. In: Delp, E.J., Wong, P.W. (eds.) Proceedings of SPIE: Security and Watermarking of Multimedia Content IX, vol. 6505 (2007), 65050U
Kharrazi, M., Sencar, H.T., Memon, N.: Blind source camera identification. In: Proceedings of the 2004 IEEE International Conference on Image Processing (ICIP 2004), pp. 709–712 (2004)
Bayram, S., Sencar, H.T., Memon, N.: Improvements on source camera-model identification based on CFA. In: Proceedings of the WG 11.9 International Conference on Digital Forensics (2006)
Çeliktutan, O., Avcibas, İ., Sankur, B.: Blind identification of cellular phone cameras. In: Delp, E.J., Wong, P.W. (eds.) Proceedings of SPIE: Security and Watermarking of Multimedia Content IX, vol. 6505 (2007), 65051H
Swaminathan, A., Wu, M., Liu, K.J.R.: Nonintrusive component forensics of visual sensors using output images. IEEE Transactions on Information Forensics and Security 2(1), 91–106 (2007)
Filler, T., Fridrich, J., Goljan, M.: Using sensor pattern noise for camera model identification. In: Proceedings of the 2008 IEEE International Conference on Image Processing (ICIP 2008), pp. 1296–1299 (2008)
Lukáš, J., Fridrich, J., Goljan, M.: Determining digital image origin using sensor imperfections. In: Said, A., Apostolopoulus, J.G. (eds.) Proceedings of SPIE: Image and Video Communications and Processing, vol. 5685, pp. 249–260 (2005)
Chen, M., Fridrich, J., Goljan, M.: Digital imaging sensor identification (further study). In: Delp, E.J., Wong, P.W. (eds.) Proceedings of SPIE: Security and Watermarking of Multimedia Content IX, vol. 6505 (2007), 65050P
Gloe, T., Franz, E., Winkler, A.: Forensics for flatbed scanners. In: Delp, E.J., Wong, P.W. (eds.) Proceedings of SPIE: Security, Steganography, and Watermarking of Multimedia Contents IX, vol. 6505 (2007), 65051I
Gou, H., Swaminathan, A., Wu, M.: Robust scanner identification based on noise features. In: Delp, E.J., Wong, P.W. (eds.) Proceedings of SPIE: Security and Watermarking of Multimedia Content IX, vol. 6505 (2007), 65050S
Khanna, N., Mikkikineni, A.K., Chiu, G.T.C., Allebach, J.P., Delp, E.J.: Scanner identification using sensor pattern noise. In: Delp, E.J., Wong, P.W. (eds.) Proceedings of SPIE: Security and Watermarking of Multimedia Content IX, vol. 6505 (2007), 65051K
Goljan, M., Fridrich, J., Filler, T.: Large scale test of sensor fingerprint camera identification. In: Delp, E.J., Dittmann, J., Memon, N., Wong, P.W. (eds.) Proceedings of SPIE: Media Forensics and Security XI, vol. 7254, 7254–18 (2009)
Çeliktutan, O., Sankur, B., Avcibas, İ.: Blind identification of source cell-phone model. IEEE Transactions on Information Forensics and Security 3(3), 553–566 (2008)
Farid, H., Lyu, S.: Higher-order wavelet statistics and their application to digital forensics. In: IEEE Workshop on Statistical Analysis in Computer Vision (in conjunction with CVPR) (2003)
Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Gloe, T., Böhme, R.: The ‘Dresden’ image database for benchmarking digital image forensics. Submitted to the 25th Symposium on Applied Computing, ACM SAC 2010 (2010)
Adams, J., Parulski, K., Spaulding, K.: Color processing in digital cameras. IEEE Micro. 18(6), 20–30 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gloe, T., Borowka, K., Winkler, A. (2009). Feature-Based Camera Model Identification Works in Practice. In: Katzenbeisser, S., Sadeghi, AR. (eds) Information Hiding. IH 2009. Lecture Notes in Computer Science, vol 5806. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04431-1_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-04431-1_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04430-4
Online ISBN: 978-3-642-04431-1
eBook Packages: Computer ScienceComputer Science (R0)