Video-ACID

Modern technologies have made the capture and sharing of digital video commonplace; the combination of modern smartphones, cloud storage, and social media platforms have enabled video to become a primary source of information for many people and institutions. As a result, it is important to be able to verify the authenticity and source of this information, including identifying the source camera model that captured it. While a variety of forensic techniques have been developed for digital images, less research has been conducted towards the forensic analysis of videos. In part, this is due to a lack of standard digital video databases, which are necessary to develop and evaluate state-of-the-art video forensic algorithms. To address this need, in this paper we present the Video Authentication and Camera IDentification (VideoACID) database, a large collection of videos specifically collected for the development of camera model identification algorithms. The Video-ACID database contains over 12,000 videos from 46 physical devices representing 36 unique camera models. Videos in this database are hand collected in a diversity of real-world scenarios, are unedited, and have known and trusted provenance. In this paper, we describe the qualities, structure, and collection procedure of Video-ACID, which includes clearly marked videos for evaluating camera model identification algorithms. Finally, we provide baseline camera model identification results on these evaluation videos using a state-of-the-art deep-learning technique. The Video-ACID database is publicly available at misl.ece.drexel.edu/video-acid