Deep learning for source camera identification on mobile devices

TL;DR

This paper introduces a deep learning-based method using CNNs to accurately identify the source mobile camera and device from images, demonstrating high robustness and 98% accuracy.

Contribution

It presents a novel CNN architecture specifically designed for mobile camera fingerprinting, improving identification accuracy and robustness over previous methods.

Findings

Achieved 98% accuracy in identifying mobile devices.

Proved robustness of the CNN approach across different configurations.

Validated on MICHE-I dataset with consistent results.

Abstract

In the present paper, we propose a source camera identification method for mobile devices based on deep learning. Recently, convolutional neural networks (CNNs) have shown a remarkable performance on several tasks such as image recognition, video analysis or natural language processing. A CNN consists on a set of layers where each layer is composed by a set of high pass filters which are applied all over the input image. This convolution process provides the unique ability to extract features automatically from data and to learn from those features. Our proposal describes a CNN architecture which is able to infer the noise pattern of mobile camera sensors (also known as camera fingerprint) with the aim at detecting and identifying not only the mobile device used to capture an image (with a 98\% of accuracy), but also from which embedded camera the image was captured. More specifically, we provide an extensive analysis on the proposed architecture considering different configurations. The experiment has been carried out using the images captured from different mobile devices cameras (MICHE-I Dataset was used) and the obtained results have proved the robustness of the proposed method.