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Published, Version of Record (VoR)CC BY-NC-ND V4.0, Open
Journal article
The Video Authentication and Camera Identification Database: A New Database for Video Forensics
IEEE access, v 7, pp 76937-76948
2019
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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 toward 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. In this paper, to address this need, we present the video authentication and camera identification (video-ACID) 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 the state-of-the-art deep-learning techniques. The Video-ACID database is publicly available at misl.ece.drexel.edu/video-acid.
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Details
- Title
- The Video Authentication and Camera Identification Database: A New Database for Video Forensics
- Creators
- Brian C Hosler - Drexel UniversityXinwei Zhao - Drexel UniversityOwen Mayer - Drexel UniversityChen Chen - Drexel UniversityJames A Shackleford - Drexel UniversityMatthew C Stamm - Drexel University
- Publication Details
- IEEE access, v 7, pp 76937-76948
- Publisher
- IEEE
- Grant note
- 1553610 / National Science Foundation (10.13039/100000001) W911NF-17-2-0087 / Army Research Office (10.13039/100000183)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000473631600001
- Scopus ID
- 2-s2.0-85068269512
- Other Identifier
- 991019167748304721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Information Systems
- Engineering, Electrical & Electronic
- Telecommunications