Camera Model Identification Based on Deep Learning


KIPS Transactions on Software and Data Engineering, Vol. 8, No. 10, pp. 411-420, Oct. 2019
https://doi.org/10.3745/KTSDE.2019.8.10.411, Full Text:
Keywords: Deep Learning, Camera Model Identification, Convolutional Neural Network, High Pass Filter, Gray Level Co-Occurrence Matrix
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.


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Cite this article
[IEEE Style]
S. H. Lee, D. H. Kim and H. Lee, "Camera Model Identification Based on Deep Learning," KIPS Transactions on Software and Data Engineering, vol. 8, no. 10, pp. 411-420, 2019. DOI: https://doi.org/10.3745/KTSDE.2019.8.10.411.

[ACM Style]
Soo Hyeon Lee, Dong Hyun Kim, and Hae-Yeoun Lee. 2019. Camera Model Identification Based on Deep Learning. KIPS Transactions on Software and Data Engineering, 8, 10, (2019), 411-420. DOI: https://doi.org/10.3745/KTSDE.2019.8.10.411.