Image Forensics has already achieved great results for the source camera
identification task on images. Standard approaches for data coming from Social
Network Platforms cannot be applied due to different processes involved (e.g.,
scaling, compression, etc.). Over 1 billion images are shared each day on the
Internet and obtaining information about their history from the moment they
were acquired could be exploited for investigation purposes. In this paper, a
classification engine for the reconstruction of the history of an image, is
presented. Specifically, exploiting K-NN and decision trees classifiers and
a-priori knowledge acquired through image analysis, we propose an automatic
approach that can understand which Social Network Platform has processed an
image and the software application used to perform the image upload. The engine
makes use of proper alterations introduced by each platform as features.
Results, in terms of global accuracy on a dataset of 2720 images, confirm the
effectiveness of the proposed strategy.Comment: 6 pages, 1 figur
The growth of popularity of Social Network Services (SNSs) opened new perspectives in many research fields, including the emerging area of Multimedia Forensics. In particular, the huge amount of images uploaded to the social networks can represent a significant source of evidence for investigations, if properly processed. This work aims to exploit the algorithms and techniques behind the uploading process of a picture on Facebook, in order to find out if any of the involved steps (resizing, compression, renaming, etc.) leaves a trail on the picture itself, so to infer proper hypotheses about the authenticity and other forensic aspects of the pipeline.
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