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http://dx.doi.org/10.3745/KTSDE.2019.8.10.411

Camera Model Identification Based on Deep Learning  

Lee, Soo Hyeon (금오공과대학교 소프트웨어공학과)
Kim, Dong Hyun (금오공과대학교 소프트웨어공학과)
Lee, Hae-Yeoun (금오공과대학교 컴퓨터소프트웨어공학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.8, no.10, 2019 , pp. 411-420 More about this Journal
Abstract
Camera model identification has been a subject of steady study in the field of digital forensics. Among the increasingly sophisticated crimes, crimes such as illegal filming are taking up a high number of crimes because they are hard to detect as cameras become smaller. Therefore, technology that can specify which camera a particular image was taken on could be used as evidence to prove a criminal's suspicion when a criminal denies his or her criminal behavior. This paper proposes a deep learning model to identify the camera model used to acquire the image. The proposed model consists of four convolution layers and two fully connection layers, and a high pass filter is used as a filter for data pre-processing. To verify the performance of the proposed model, Dresden Image Database was used and the dataset was generated by applying the sequential partition method. To show the performance of the proposed model, it is compared with existing studies using 3 layers model or model with GLCM. The proposed model achieves 98% accuracy which is similar to that of the latest technology.
Keywords
Deep Learning; Camera Model Identification; Convolutional Neural Network; High Pass Filter; Gray Level Co-Occurrence Matrix;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 M. Kharrazi, H.-T. Sencar, and N. Memon, "Blind source camera identification," Proceedings of the International Conference on Image Processing, Vol.1, pp.709-712, 2004.
2 S. Bayram, H. Sencar, N. Memon, and I. Avcibas, "Source camera identification based on CFA interpolation," Proceedings of the IEEE International Conference on Image Processing, Vol.3, pp.III-69, 2005.
3 A. Popescu and H. Farid, "Exposing Digital Forgeries by Detecting Traces of Re-sampling," IEEE Transactions on Signal Processing, Vol.53, No.2, 2005.
4 K.-S. Choi, E.-Y. Lam, and K.-K. Wong, "Source camera identification using footprints from lens aberration," Proceedings of SPIE, Digital Photography II, Vol.6069, pp. 60690J, 2006.
5 J. Lukas, J. Fridrich, and M. Goljan, "Digital camera identification from sensor pattern noise," IEEE Transactions on Information Forensics and Security, Vol.1 No.2, pp.205-214, 2006.   DOI
6 K. Bolouri, A. Azmoodeh, A. Dehghantanha, and M. Firouzmand, "Internet of things camera identification algorithm based on sensor pattern noise using color filter array and wavelet transform," In Handbook of Big Data and IoT Security, Springer, Cham, pp.211-223, 2019.
7 A. Tuama, F. Comby, and M. Chaumont, "Camera model identification with the use of deep convolutional neural network," Proceedings of the IEEE International Workshop on Information Forensics and Security, pp.1-6, 2016.
8 A. Krizhevsky, I. Sutskever, and G.-E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in Neural Information Processing Systems, pp. 1097-1105, 2012.
9 C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S.-E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, "Going deeper with convolutions," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9, 2015.
10 L. Bondi, L. Baroffio, D. Guera, P. Bestagini, E.-J. Delp, and S. Tubaro, "First steps toward camera model identification with convolutional neural networks," IEEE Signal Processing Letters, Vol.24, No.3, pp.259-263, 2017.   DOI
11 D. Freire-Obregon, F. Narducci, S. Barra, and M. Castrillon-Santana, "Deep learning for source camera identification on mobile devices," Pattern Recognition Letters, Vol.126, pp.86-91, 2018.   DOI
12 S.-H. Lee and H.-Y. Lee, "Printer Identification Methods Using Global and Local Feature-Based Deep Learning," KIPS Transactions on Software and Data Engineering, Vol. 8, No.1, pp.37-44, 2019.   DOI
13 J.-Y. Baek, H.-S. Lee, S.-G. Kong, J.-H. Choi, Y.-M. Yang, and H.-Y. Lee, "Color Laser Printer Identification through Discrete Wavelet Transform and Gray Level Co-occurrence Matrix," The KIPS Transactions: Part B, Vol.17, No.3, pp 197-206, 2010.
14 Dresden Image Database, [Internet], http://forensics.inf.tudresden.de/ddimgdb/
15 B. Hosler, O. Mayer, B. Bayar, X. Zhao, C. Chen, J.-A. Shackleford, and M.-C. Stamm, "A Video Camera Model Identification System Using Deep Learning and Fusion," In ICASSP 2019-2019 IEEE International Conference on Acoustics, pp.8271-8275, 2019.
16 V. Nair and G.-E. Hinton, "Rectified linear units improve restricted boltzmann machines," Proceedings of the International Conference on Machine Learning, pp.807-814, 2010.
17 N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: a simple way to prevent neural networks from overfitting," Journal of Machine Learning Research, Vol.15, No.1, pp.1929-1958, 2014.