Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/41417
Title: Deep learning for source camera identification on mobile devices
Authors: Freire-Obregón, David 
Narducci, Fabio
Barra, Silvio
Castrillón-Santana, Modesto 
UNESCO Clasification: 120325 Diseño de sistemas sensores
120304 Inteligencia artificial
Keywords: Iris recognition
Networks
Source camera identification
Convolutional neural networks
Mobile devices
Deep learning
Issue Date: 2019
Journal: Pattern Recognition Letters 
Abstract: In the present paper, we propose a source camera identification (SCI) 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 device cameras (MICHE-I Dataset) and the obtained results have proved the robustness of the proposed method.
URI: http://hdl.handle.net/10553/41417
ISSN: 0167-8655
DOI: 10.1016/j.patrec.2018.01.005
Source: Pattern Recognition Letters [ISSN 0167-8655], v.126, p. 86-91 (2019)
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