ForensiCam-215K: A Large Scale Image and Video Dataset for Forensic Analysis

Suwen Du, Pengpeng Yang, Daniele Baracchi, Jinglian Jin, Dasara Shullani, Alessandro Piva

Abstract: Determining the origin of a digital image or video, namely device source identification, is widely used in courtroom evidence and copyright protection. Currently, device source identification primarily focuses on images captured using single camera with default settings. However, with the advancement of imaging technology, there is a large number of smartphones equipped with multiple cameras and various shooting modes for acquiring images, which may pose a significant challenge to device source identification. Therefore, to assess the performance of image source identification algorithm for modern smartphones and promote further research, it is crucial to build a dataset of image and video captured by modern smartphones. In this paper, we present a large-scale image and video dataset for forensic analysis, ForensiCam-215K. The dataset includes over 215K media contents captured by 130 modern smartphones of 10 major brands. We used the latest equipment to capture images from the main, wide-angle, and telephoto cameras in six different shooting modes, and the media were collected under a strictly controlled procedure to reduce the bias caused by differences in the acquisition process between different devices. Additionally, we used the Photo Response Non-Uniformity (PRNU) method to perform device source identification tests on the dataset. The results indicate that device source identification is a challenging task especially for images and videos captured by smartphones with multiple cameras and various shooting modes. The dataset will be released as open-source and freely available for use by the multimedia forensics research community at https://github.com/dswdsw21072/ForensiCam-215K.

External IDs:dblp:conf/icassp/Du0BJSP25