FloreView: an image and video dataset for forensic analysis

Abstract

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 fingerprint have been developed to address these problems, they are frequently made obsolete by new customized acquisition pipelines implemented by manufacturers. Therefore, to assess the performance of their tools, 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 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 to show how the proposed dataset can be effectively used for forensic tasks.

BibTeX

@article{baracchi2023floreview,
    author={Baracchi, Daniele and Shullani, Dasara and Iuliani, Massimo and Piva, Alessandro},
    journal={IEEE Access}, 
    title={FloreView: an image and video dataset for forensic analysis}, 
    year={2023},
    volume={11},
    number={},
    pages={109267-109282},
    doi={10.1109/ACCESS.2023.3321991}
}

Acknowledgments

This work was supported in part by the Italian Ministry of Universities and Research (MUR) under Grant 2017Z595XS, and in part by the Defense Advanced Research Projects Agency (DARPA) under Agreement No. HR00112090136.

The authors would like to thank the people involved in the data collection: Chiara Albisani, Alberto Arienzo, Francesco Baffa, Francesco Barbieri, Alice Cavaliere, Guido Ciapetti, Andrea Cimbalo, Andrea Croce, Andrea Desideri, Elia Ducceschi, Carolina Di Quinzio, Francesco Fantechi, Yoshihisa Furushita, Lucia Giorgi, Simone Izzo, Graziano Manduzio, Stefano Martina, Elio Marunti, Lorenzo Massai, Maria Teresa Nardoni, Daniele Narducci, Alessandro Nozzoli, Simone Pezzulla, Edoardo Putti, Claudia Raffaelli, Maria Scarano, Tomaso Trinci, Alessandro Ugolini, Xingyi Yu, Riccardo Zucchini.