Abstract
State-of-the-art digital forensic techniques for camera model identification draw attention on different sets of features to assign an image to the employed source model. This paper complements existing work, by a comprehensive evaluation of known feature sets employing a large set of 26 camera models with altogether 74 devices. We achieved the highest accuracies using the extended colour feature set and present several detail experiments to validate the ability of the features to separate between camera models and not between devices. Analysing more than 16,000 images, we present a comprehensive evaluation on 1) the number of required images and devices for training, 2) the influence of the number of camera models and camera settings on the detection results and 3) possibilities to handle unknown camera models when not all models coming into question are available or are even known. All experiments in this paper suggest: feature-based forensic 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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Böhme, R., Freiling, F., Gloe, T., Kirchner, M.: Multimedia Forensics Is Not Computer Forensics. In: Geradts, Z.J.M.H., Franke, K.Y., Veenman, C.J. (eds.) IWCF 2009. LNCS, vol. 5718, pp. 90–103. Springer, Heidelberg (2009)
Khanna, N., Mikkilineni, 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, p. 65050U (2007)
Lyu, S., Farid, H.: How realistic is photorealistic? IEEE Transactions on Signal Processing 53(2), 845–850 (2005)
Fridrich, J.: Digital image forensics. IEEE Signal Processing Magazine 26(2), 26–37 (2009)
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, p. 65051I (2007)
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, p. 65050S (2007)
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, p. 65051K (2007)
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, pp. 7254–18 (2009)
Johnson, M.K., Farid, H.: Exposing digital forgeries through chromatic aberration. In: Proceedings of the Multimedia and Security Workshop (MM&Sec 2006), pp. 48–55 (2006)
Gloe, T., Borowka, K., Winkler, A.: Efficient estimation and large-scale evaluation of lateral chromatic aberration for digital image forensics. In: Memon, N.D., Dittmann, J., Alattar, A.M., Delp, E.J. (eds.) Proceedings of SPIE: Media Forensics and Security II, vol. 7541, pp. 7541–7 (2010)
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)
Cao, H., Kot, A.C.: Accurate detection of demosaicing regularity for digital image forensics. IEEE Transactions on Information Forensics and Security 4(4), 899–910 (2009)
Farid, H.: Digital image ballistics from JPEG quantization: A followup study. Technical Report TR2008-638, Department of Computer Science, Dartmouth College, Hanover, NH, USA (2008)
Kee, E., Johnson, M.K., Farid, H.: Digital image authentication from JPEG headers. IEEE Transactions on Information Forensics and Security 6(3), 1066–1075 (2011)
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)
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, p. 65051H (2007)
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)
Gloe, T., Borowka, K., Winkler, A.: Feature-Based Camera Model Identification Works in Practice – Results of a Comprehensive Evaluation Study. In: Katzenbeisser, S., Sadeghi, A.-R. (eds.) IH 2009. LNCS, vol. 5806, pp. 262–276. Springer, Heidelberg (2009)
Gloe, T., Böhme, R.: The ‘Dresden Image Database’ for benchmarking digital image forensics. In: Proceedings of the 25th Symposium on Applied Computing (ACM SAC), vol. 2, pp. 1585–1591 (2010)
Gloe, T., Cebron, N., Böhme, R.: An ML perspective on feature-based forensic camera model identification. In: DAGM Workshop Pattern Recognition for IT Security, Darmstadt, Germany, September 21 (2010)
Farid, H., Lyu, S.: Higher-order wavelet statistics and their application to digital forensics. In: IEEE Workshop on Statistical Analysis in Computer Vision (2003)
Avcıbaş, İ., Sankur, B., Sayood, K.: Statistical evaluation of image quality measures. Journal of Electronic Imaging 11(2), 206–223 (2002)
Avcibas, İ., Kharrazi, M., Memon, N.D., Sankur, B.: Image steganalysis with binary similarity measures. EURASIP Journal on Applied Signal Processing 2005(17), 2749–2757 (2005)
Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. Software (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Pudil, P., Novovičová, J., Kittler, J.: Floating search methods in feature selection. Pattern Recognition Letters 15(11), 1119–1125 (1994)
Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Computation 13(7), 1443–1471 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gloe, T. (2012). Feature-Based Forensic Camera Model Identification. In: Shi, Y.Q., Katzenbeisser, S. (eds) Transactions on Data Hiding and Multimedia Security VIII. Lecture Notes in Computer Science, vol 7228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31971-6_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-31971-6_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31970-9
Online ISBN: 978-3-642-31971-6
eBook Packages: Computer ScienceComputer Science (R0)