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Linking a digital image or video to its originating device, or checking the content integrity still represent challenging forensic tasks. Even though several technologies based on metadata, file format, and sensor noise have been developed to address these problems, current methods are frequently made obsolete by new customized acquisition pipelines implemented by manufacturers. Therefore, to assess the performance of the available tools and push the research activity, researchers continuously need new datasets containing contents captured with recent technologies. In this paper, we present a new image and video dataset for forensic analysis. Data, acquired by the most recent acquisition devices, were collected under strictly controlled procedures designed to limit the bias induced by differences in the acquisition process between different devices. The dataset includes over 9000 media contents captured by 46 smartphones of 11 major brands. For each device, we collected at least 100 unique natural images, 30 unique natural videos, 30 flat images, and 4 flat videos. Great care has been taken in collecting data that can be used for multiple forensic tasks; moreover, images and videos have been carefully organized so that FloreView could be used by the community immediately and effortlessly. Finally, two case studies related to image source identification and video brand identification have been performed, using state-of-the-art methods, to show how the proposed dataset can be effectively used for forensic tasks.

FloreView: an image and video dataset for forensic analysis