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Recognizing information about the origin of a digital image has been individuated as a crucial task to be tackled by the image forensic scientific community. Understanding something on the previous history of an image could be strategic to address any successive assessment to be made on it: knowing the kind of device used for acquisition or, better, the model of the camera could focus investigations in a specific direction. Sometimes just revealing that a determined post-processing such as an interpolation or a filtering has been performed on an image could be of fundamental importance to go back to its provenance. This paper locates in such a context and proposes an innovative method to inquire if an image derives from a social network and, in particular, try to distinguish from which one has been downloaded. The technique is based on the assumption that each social network applies a peculiar and mostly unknown manipulation that however leaves some distinctive traces on the image; such traces can be extracted to feature every platform. By resorting at trained classifiers, the presented methodology is satisfactorily able to discern different social network origin. Experimental results carried out on diverse image datasets and in various operative conditions witness that such a distinction is possible. In addition, the proposed method is also able to go back to the original JPEG quality factor the image had before being uploaded on a social network.

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Many everyday activities involve the exchange of confidential information through the use of a smartphone in mobility i.e. sending on e-mail, checking bank account, buying on-line, accessing cloud platforms, health monitoring. This demonstrates how security issues related to these operations are a major challenge in our society and in particular in the cybersecurity domain. The proposed paper focuses on the use of the smartphone intrinsic and physical characteristics as a mean to build a smartphone fingerprint to enable devices identification. The basic idea proposed in this paper is to investigate how to generate a specific fingerprint that allows to distinctively and reliably characterize each smartphone. In particular, the accelerometer, the gyroscope, the magnetometer and the audio system (microphone-speaker) are taken into account to build up a composite fingerprint based on a set of their distinctive features. Many experiments have been carried out, by analyzing different classification methods, diverse features combination configurations and operative scenarios. Satisfactory results have been obtained showing that the combination of such sensors improves smartphone distinctiveness.