References
- 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.
- 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.
- A. Popescu and H. Farid, "Exposing Digital Forgeries by Detecting Traces of Re-sampling," IEEE Transactions on Signal Processing, Vol.53, No.2, 2005.
- 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.
- 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. https://doi.org/10.1109/TIFS.2006.873602
- 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.
- 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.
- 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.
- 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.
- 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. https://doi.org/10.1109/LSP.2016.2641006
- 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. https://doi.org/10.1016/j.patrec.2018.01.005
- 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. https://doi.org/10.3745/KTSDE.2019.8.1.37
- 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.
- 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.
- 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.
- 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.
- Dresden Image Database, [Internet], http://forensics.inf.tudresden.de/ddimgdb/