Recovering information about the history of a digital content, such as an image or a video, can be strategic to address an investigation from the early stages. Storage devices, smart-phones and PCs, belonging to a suspect, are usually confiscated as soon as a warrant is issued. Any multimedia content found is analyzed in depth, in order to trace back its provenance and, if possible, its original source. This is particularly important when dealing with social networks, where most of the user-generated photos and videos are uploaded and shared daily. Being able to discern if images are downloaded from a social network or directly captured by a digital camera, can be crucial in leading consecutive investigations. In this paper, we propose a novel method based on convolutional neural networks (CNN) to determine the image provenance, whether it originates from a social network, a messaging application or directly from a photo-camera. By considering only the visual content, the method works irrespective of an eventual manipulation of metadata performed by an attacker. We have tested the proposed technique on three publicly available datasets of images downloaded from seven popular social networks, obtaining state-of-the-art results.

Tracing images back to their social network of origin: A CNN-based approach / Amerini, Irene; Uricchio, Tiberio; Caldelli, Roberto. - (2017), pp. 1-6. (Intervento presentato al convegno 9th IEEE Workshop on Information Forensics and Security (WIFS) tenutosi a Ravanna; Italy) [10.1109/WIFS.2017.8267660].

Tracing images back to their social network of origin: A CNN-based approach

Amerini, Irene
;
2017

Abstract

Recovering information about the history of a digital content, such as an image or a video, can be strategic to address an investigation from the early stages. Storage devices, smart-phones and PCs, belonging to a suspect, are usually confiscated as soon as a warrant is issued. Any multimedia content found is analyzed in depth, in order to trace back its provenance and, if possible, its original source. This is particularly important when dealing with social networks, where most of the user-generated photos and videos are uploaded and shared daily. Being able to discern if images are downloaded from a social network or directly captured by a digital camera, can be crucial in leading consecutive investigations. In this paper, we propose a novel method based on convolutional neural networks (CNN) to determine the image provenance, whether it originates from a social network, a messaging application or directly from a photo-camera. By considering only the visual content, the method works irrespective of an eventual manipulation of metadata performed by an attacker. We have tested the proposed technique on three publicly available datasets of images downloaded from seven popular social networks, obtaining state-of-the-art results.
2017
9th IEEE Workshop on Information Forensics and Security (WIFS)
Computer Networks and Communications; Information Systems; Information Systems and Management; Safety; Risk; Reliability and Quality
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Tracing images back to their social network of origin: A CNN-based approach / Amerini, Irene; Uricchio, Tiberio; Caldelli, Roberto. - (2017), pp. 1-6. (Intervento presentato al convegno 9th IEEE Workshop on Information Forensics and Security (WIFS) tenutosi a Ravanna; Italy) [10.1109/WIFS.2017.8267660].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1324974
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