  Knowledge of source smartphone corresponding to a document image can be
helpful in a variety of applications including copyright infringement,
ownership attribution, leak identification and usage restriction. In this
letter, we investigate a convolutional neural network-based approach to solve
source smartphone identification problem for printed text documents which have
been captured by smartphone cameras and shared over messaging platform. In
absence of any publicly available dataset addressing this problem, we introduce
a new image dataset consisting of 315 images of documents printed in three
different fonts, captured using 21 smartphones and shared over WhatsApp.
Experiments conducted on this dataset demonstrate that, in all scenarios, the
proposed system performs as well as or better than the state-of-the-art system
based on handcrafted features and classification of letters extracted from
document images. The new dataset and code of the proposed system will be made
publicly available along with this letter's publication, presently they are
submitted for review.
