Abstract
In recent years, the field of digital imaging has made significant progress, so that today every smartphone has a built-in video camera that allows you to record high-quality video for free and without restrictions. On the other hand, rapidly growing internet technology has contributed significantly to the widespread use of digital video via web-based multimedia systems and mobile smartphone applications such as YouTube, Facebook, Twitter, WhatsApp, etc. However, as the recording and distribution of digital videos have become affordable nowadays, security issues have become threatening and spread worldwide. One of the security issues is identifying source cameras on videos. There are some new challenges that should be addressed in this area. One of the new challenges is individual source camera identification (ISCI), which focuses on identifying each device regardless of its model. The first step towards solving the problems is a popular video database recorded by modern smartphone devices, which can also be used for deep learning methods that are growing rapidly in the field of source camera identification. In this paper, a smartphone video database named Qatar University Forensic Video Database (QUFVD) is introduced. The QUFVD includes 6000 videos from 20 modern smartphone representing five brands, each brand has two models, and each model has two identical smartphone devices. This database is suitable for evaluating different techniques such as deep learning methods for video source smartphone identification and verification. To evaluate the QUFVD, a series of experiments to identify source cameras using a deep learning technique are conducted. The results show that improvements are essential for the ISCI scenario on video.
Highlights
C ELLPHONE has developed rapidly over the past century due to its economic advantages, functionality and ease of access [1]
There are several databases for identifying source cameras for images [15], [16], there are few databases for videos. For new challenges such as individual source camera identification (ISCI) and deep source camera identification analysis that focus on video, it is essential to have a database to perform new methods on video
Since most video databases focus on videos recorded with a VCR, and among them there is only one database for smartphones (Daxing) [1], in order to give new tasks to the databases, we focus on presenting a smartphone database for videos
Summary
C ELLPHONE has developed rapidly over the past century due to its economic advantages, functionality and ease of access [1]. It should be noted that methods based on images cannot be applied directly to videos [10]–[12] This is due to some challenges such as compression, stabilization, scaling, and cropping, as well as the differences between frame types that can occur when producing a video. As a result of the challenges and advances in research in the field of forensic video analysis, such as deep learning methods, there is a need for standard databases that allow researchers to more compare techniques using the same experimental protocols. For new challenges such as ISCI and deep source camera identification analysis that focus on video, it is essential to have a database to perform new methods on video.
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