Abstract
Camera model identification is of interest for many applications. In-camera processes, specific of each model, leave traces that can be captured by features designed ad hoc, and used for reliable classification. In this work we investigate on the use of blind features based on the analysis of image residuals. In particular, features are extracted locally based on co-occurrence matrices of selected neighbors and then used to train an SVM classifier. Experiments on the well-known Dresden database show this approach to provide state-of-the-art performances.
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Kirchner, M., Gloe, T.: Forensic camera model identification. In: Ho, T., Li, S. (eds.) Handbook of Digital Forensics of Multimedia Data and Devices. Wiley-IEEE Press (2015)
Lukàš, J., Fridrich, J., Goljan, M.: Digital camera identification from sensor pattern noise. IEEE Transactions on Information Forensics and Security 1(2), 205–214 (2006)
Filler, T., Fridrich, J., Goljan, M.: Using sensor pattern noise for camera model identification. In: IEEE International Conference on Image Processing, pp. 1296–1299 (2008)
Bayram, S., Sencar, H., Memon, N., Avcibas, I.: Source camera identification based on CFA interpolation. In: IEEE Int. Conference on Image Processing, pp. 69–72 (2005)
Popescu, A., Farid, H.: Exposing digital forgeries by detecting traces of resampling. IEEE Transactions on Signal Processing 53(2), 758–767 (2005)
Swaminathan, A., Wu, M., Liu, K.J.R.: Rich models for steganalysis of digital images. IEEE Transactions on Information Forensics and Security 2(1), 91–105 (2007)
Cao, H., Kot, A.: Accurate detection of demosaicing regularity for digital image forensics. IEEE Trans. on Information Forensics and Security 4(4), 899–910 (2009)
Bayram, S., Sencar, H., Memon, N.: Improvements on source camera-model identification based on CFA. In: Advances in Digital Forensics II, IFIP International Conference on Digital Forensics, pp. 289–299 (2006)
Fan, N., Jin, C., Huang, Y.: Source Camera identification by JPEG compression statistics for image forensics. In: TENCON, pp. 1–4 (2006)
Thai, T., Retraint, F., Cogranne, R.: Camera model identification based on dct coefficient statistics. Digital Signal Processing 4, 88–100 (2015)
Van, L., Emmanuel, S., Kankanhalli, M.: Identifying source cell phone using chromatic aberration. In: IEEE International Conference on Multimedia and Expo, pp. 883–886 (2007)
Thai, T., Cogranne, R., Retraint, F.: Camera model identification based on the heteroscedastic noise model. IEEE Transactions on Image Processing 23(1), 250–263 (2014)
Kharrazi, M., Sencar, H., Memon, N.: Blind source camera identification. In: IEEE International Conference on Image Processing, pp. 709–712 (2004)
Avcibaş, I., Memon, N., Sankur, B.: Steganalysis using image quality metrics. IEEE Transactions on Image Processing 12(2), 221–229 (2003)
Çeliktutan, O., Sankur, B., Avcibaş, I.: Blind identification of source cell-phone model. IEEE Transactions on Information Forensics and Security 3(3), 553–566 (2008)
Lyu, S., Farid, H.: Steganalysis using higher-order image statistics. IEEE Transactions on Information Forensics and Security 1(1), 111–119 (2006)
Gloe, T.: Feature-based forensic camera model identification. In: Shi, Y.Q., Katzenbeisser, S. (eds.) Transactions on DHMS VIII. LNCS, vol. 7228, pp. 42–62. Springer, Heidelberg (2012)
Gloe, T., Böhme, R.: The Dresden image database for benchmarking digital image forensics. Journal of Digital Forensic Practice 3(2–4), 150–159 (2010)
Xu, G., Shi, Y.: Camera model identification using local binary patterns. In: IEEE International Conference on Multimedia and Expo, pp. 392–397 (2012)
Gragnaniello, D., Poggi, G., Sansone, C., Verdoliva, L.: An investigation of local descriptors for biometric spoofing detection. IEEE Transactions on Information Forensics and Security 10(4), 849–863 (2015)
Xu, G., Gao, S., Shi, Y.Q., Hu, R.M., Su, W.: Camera-model identification using markovian transition probability matrix. In: Ho, A.T.S., Shi, Y.Q., Kim, H.J., Barni, M. (eds.) IWDW 2009. LNCS, vol. 5703, pp. 294–307. Springer, Heidelberg (2009)
Shi, Y.Q., Chen, C.-H., Chen, W.: A markov process based approach to effective attacking JPEG steganography. In: Camenisch, J.L., Collberg, C.S., Johnson, N.F., Sallee, P. (eds.) IH 2006. LNCS, vol. 4437, pp. 249–264. Springer, Heidelberg (2007)
Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Transactions on Information Forensics and Security 7, 868–882 (2012)
Kirchner, M., Fridrich, J.: On detection of median filtering in images. In: SPIE, Electronic Imaging, Media Forensics and Security XII, pp. 101–112 (2010)
Verdoliva, L., Cozzolino, D., Poggi, G.: A feature-based approach for image tampering detection and localization. In: IEEE Workshop on Information Forensics and Security, pp. 149–154 (2014)
Chierchia, G., Parrilli, S., Poggi, G., Sansone, C., Verdoliva, L.: On the influence of denoising in PRNU based forgery detection. In: 2nd ACM workshop on Multimedia in Forensics, Security and Intelligence, pp. 117–122 (2010)
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Marra, F., Poggi, G., Sansone, C., Verdoliva, L. (2015). Evaluation of Residual-Based Local Features for Camera Model Identification. In: Murino, V., Puppo, E., Sona, D., Cristani, M., Sansone, C. (eds) New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops. ICIAP 2015. Lecture Notes in Computer Science(), vol 9281. Springer, Cham. https://doi.org/10.1007/978-3-319-23222-5_2
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