Prnu Pattern Alignment for Images and Videos Based on Scene Content
Fabio Bellavia, Massimo Iuliani, Marco Fanfani, Carlo Colombo, Alessandro Piva · 2019
This paper proposes a novel approach for registering the PRNU pattern between different camera acquisition modes by relying on the imaged scene content. First, images are aligned by establishing correspondences between local descriptors: The result can then optionally be refined by maximizing the PRNU correlation. Comparative evaluations show that this approach outperforms those based on brute-force and particle swarm optimization in terms of reliability, accuracy and speed. The proposed scene-based approach for PRNU pattern alignment is suitable for video source identification in multimedia forensics applications.