Digital forensics is a relatively new research area which aims at authenticating digital media by detecting possible digital forgeries. Indeed, the ever increasing availability of multimedia data on the web, coupled with the great advances reached by computer graphical tools, makes the modification of an image and the creation of visually compelling forgeries an easy task for any user. This in turns creates the need of reliable tools to validate the trustworthiness of the represented information. In such a context, we present here RAISE, a large dataset of 8156 high-resolution raw images, depicting various subjects and scenarios, properly annotated and available together with accompanying metadata. Such a wide collection of untouched and diverse data is intended to become a powerful resource for, but not limited to, forensic researchers by providing a common benchmark for a fair comparison, testing and evaluation of existing and next generation forensic algorithms. In this paper we describe how RAISE has been collected and organized, discuss how digital image forensics and many other multimedia research areas may benefit of this new publicly available benchmark dataset and test a very recent forensic technique for JPEG compression detection.

We describe a new forensic technique for distinguishing between computer generated and human faces in video. This technique identifies tiny fluctuations in the appearance of a face that result from changes in blood flow. Because these changes result from the human pulse, they are unlikely to be found in computer generated imagery. We use the absence or presence of this physiological signal to distinguish computer generated from human faces.

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Abstract-We describe a geometric technique to detect physically implausible trajectories of objects in video sequences. This technique explicitly models the three-dimensional ballistic motion of objects in free-flight and the two-dimensional projection of the trajectory into the image plane of a static or moving camera. Deviations from this model provide evidence of manipulation. The technique assumes that the object's trajectory is substantially influenced only by gravity, that the image of the object's center of mass can be determined from the images, and requires that any camera motion can be estimated from background elements. The computational requirements of the algorithm are modest, and any detected inconsistencies can be illustrated in an intuitive, geometric fashion. We demonstrate the efficacy of this analysis on videos of our own creation and on videos obtained from videosharing web-sites.