Archivio istituzionale della ricerca dell'Università degli Studi di Palermo
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EnglishThis 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
Bellavia, F., Iuliani, M., Fanfani, M., Colombo, C., & Piva, A. (2019). Prnu Pattern Alignment for Images and Videos Based on Scene Content. In 2019 IEEEInternational Conferenceon Image Processing - Proceedings (pp. 91-95). The Institute of Electrical and Electronics Engineers, IEEE.
| Data di pubblicazione: | 2019 |
| Titolo: | Prnu Pattern Alignment for Images and Videos Based on Scene Content |
| Autori: | |
| Citazione: | Bellavia, F., Iuliani, M., Fanfani, M., Colombo, C., & Piva, A. (2019). Prnu Pattern Alignment for Images and Videos Based on Scene Content. In 2019 IEEEInternational Conferenceon Image Processing - Proceedings (pp. 91-95). The Institute of Electrical and Electronics Engineers, IEEE. |
| 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 |
| ISBN: | 978-1-5386-6249-6 |
| Digital Object Identifier (DOI): | 10.1109/ICIP.2019.8802990 |
| Settore Scientifico Disciplinare: | Settore INF/01 - Informatica Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni |
| Appare nelle tipologie: | 2.07 Contributo in atti di convegno pubblicato in volume |
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