Video-ACID
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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
现代科技已令数字视频的采集与分享成为常态；当代智能手机、云存储与社交媒体平台的协同应用，使得视频成为众多个人与机构的核心信息来源。在此背景下，对这类信息的真实性与来源进行验证——包括识别采集该视频的相机型号——便显得尤为关键。尽管针对数字图像已开发出多种取证技术，但针对视频的取证分析相关研究仍相对不足。究其部分缘由，在于缺乏用于开发与评估前沿视频取证算法的标准数字视频数据库。为填补这一空白，本文提出了视频认证与相机识别（Video Authentication and Camera IDentification, VideoACID）数据库，这是专为相机型号识别算法研发而采集的大规模视频数据集。VideoACID数据库包含来自46台实体设备、覆盖36种独特相机型号的逾12000段视频。库内所有视频均为在多样真实场景下手工采集所得，未经过任何编辑，且来源明确可靠。本文详细阐述了VideoACID数据库的特性、结构与采集流程，其中包含用于评估相机型号识别算法的标注视频。最终，我们采用前沿深度学习技术，在这批评估视频上给出了基准相机型号识别结果。VideoACID数据库可通过网址misl.ece.drexel.edu/video-acid公开获取。
