Source Camera Identification with a Robust Device Fingerprint: Evolution from Image-Based to Video-Based Approaches

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 17; p. 7385
• Main Authors: Manisha, Li, Chang-Tsun, Kotegar, Karunakar A.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 24.08.2023; MDPI
• Subjects: Accuracy; Algorithms; Cameras; Cellular telephones; Computer forensics; convolutional neural network; Criminal investigations; Deep learning; Forensic sciences; Hard disks; Identification; Image coding; Law enforcement; Methods; Multimedia; multimedia forensics; PRNU; source camera identification; video forensics; Voyeurism; United Kingdom > UK
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23177385

With the increasing prevalence of digital multimedia content, the need for reliable and accurate source camera identification has become crucial in applications such as digital forensics. While effective techniques exist for identifying the source camera of images, video-based source identification presents unique challenges due to disruptive effects introduced during video processing, such as compression artifacts and pixel misalignment caused by techniques like video coding and stabilization. These effects render existing approaches, which rely on high-frequency camera fingerprints like Photo Response Non-Uniformity (PRNU), inadequate for video-based identification. To address this challenge, we propose a novel approach that builds upon the image-based source identification technique. Leveraging a global stochastic fingerprint residing in the low- and mid-frequency bands, we exploit its resilience to disruptive effects in the high-frequency bands, envisioning its potential for video-based source identification. Through comprehensive evaluation on recent smartphones dataset, we establish new benchmarks for source camera model and individual device identification, surpassing state-of-the-art techniques. While conventional image-based methods struggle in video contexts, our approach unifies image and video source identification through a single framework powered by the novel non-PRNU device-specific fingerprint. This contribution expands the existing body of knowledge in the field of multimedia forensics.