@inproceedings{d6cbee65b7bc4c85942244143967559a,
title = "Prnu Pattern Alignment for Images and Videos Based on Scene Content",
abstract = "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",
author = "Fabio Bellavia and Bellavia and Marco Fanfani and Massimo Iuliani and Alessandro Piva and Carlo Colombo",
year = "2019",
language = "English",
isbn = "978-1-5386-6249-6",
series = "PROCEEDINGS - INTERNATIONAL CONFERENCE ON IMAGE PROCESSING",
pages = "91--95",
booktitle = "2019 IEEEInternational Conferenceon Image Processing - Proceedings",
}