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

First Steps Toward CNN based Source Classification of Document Images Shared Over Messaging App