Camera model identification using a deep network and a reduced edge dataset


Today, the importance of digital images as a medium for social communication is growing rapidly. Sometimes, an image needs to be authenticated by verifying its source camera model or device. Recently, deep networks have become very successful at visual pattern recognition. With this motivation, several investigators have explored the possibility of using convolutional neural networks (CNNs) for camera source identification. In this paper, we use selective preprocessing, instead of a indiscriminate one, in order not to hinder the CNN’s strong ability to learn useful features for this kind of forensic task. To generate a consistent and balanced dataset, we limit the maximum number of original images to 200 per camera model, and we discard vertically taken images. Using a relatively simple deep network structure, the proposed method achieved a better prediction accuracy—95.0%—than GoogleNet and other existing methods. Also, challenging camera models such as the Sony DSC H50 and W170 can be classified with the quite high prediction accuracies of 87.9% and 83.1%, respectively.

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This work is supported by the National Research Foundation of Korea Grant funded by Korea government (NRF-2018R1A2B6006754). We gratefully acknowledge all the people who contributed to this paper and especially the support of Government of Korea through the National Research Foundation. The Korea Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon.

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Correspondence to Sang-ug Kang.

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Kang, C., Kang, Su. Camera model identification using a deep network and a reduced edge dataset. Neural Comput & Applic 32, 13139–13146 (2020).

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  • Camera source identification
  • Deep learning
  • Balanced dataset
  • Edge detection