Source Camera Identification is a crucial problem in digital forensics, as it can help to determine the origin and credibility of digital images. However, most of the existing Source Camera Identification models are not scalable, meaning that they cannot maintain accuracy level when the number of camera models increases. Moreover, these models are vulnerable to post-processing operations, which are often applied by users or online platforms to modify or enhance images. In this paper, we propose a novel and robust Source Camera Identification model based on Convolutional Neural Networks and Photo Response Non- Uniformity fingerprints. Our model extracts three-channel Photo Response Non-Uniformity fingerprints from 320×320 image patches and feeds them to a 7-layer Convolutional Neural Network for camera model classification. Our model achieves state-of-the-art accuracy of 98.36% and 97.33% at the brand and model levels, respectively, on a large and diverse dataset of 27 camera models. Our model does not require any preprocessing steps such as patch selection, multi-classifier, or majority voting, and is robust to various image processing attacks such as double JPEG compression, smoothing, sharpening, blurring, and re-scaling.

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