Camera model identification is paramount to verify image origin and authenticity in a blind fashion. State-of-the-art techniques leverage the analysis of features describing characteristic footprints left on images by different camera models from the image acquisition pipeline (e. g.,
traces left by proprietary demosaicing strategies, etc.). Motivated by the very accurate performance achieved by feature-based methods, as well as by the progress brought by deep architectures in machine learning, we explore in this paper the possibility of taking advantage of convolutional
neural networks (CNNs) for camera model identification. More specifically, we investigate: (i) the capability of different network architectures to learn discriminant features directly from the observed images; (ii) the dependency between the amount of training data and the achieved accuracy;
(iii) the importance of selecting a correct protocol for training, validation and testing. This study shows that promising results can be obtained on small image patches training a CNN with an affordable setup (i. e., a personal computer with one dedicated GPU) in a reasonable amount of time
(i. e., approximately one hour), given that a sufficient amount of training images is available.
No References for this article.
No Supplementary Data.
No Article Media
Image forensics, Camera model identification, Source attribution, Convolutional neural network, Deep learning
Document Type: Research Article
Publication date: 29 January 2017
More about this publication?
For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.
Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.