This paper explores content-based image registration as a means of dealing with and understanding better Electronic Image Stabilization (EIS) in the context of Photo Response Non-Uniformity (PRNU) alignment. A novel and robust solution to extrapolate the transformation relating the different image output formats for a given device model is proposed. This general approach can be adapted to specifically extract the scale factor (and, when appropriate, the translation) so as to align native resolution images to video frames, with or without EIS on, and proceed to compare PRNU patterns. Comparative evaluations show that the proposed approach outperforms those based on brute-force and particle swarm optimization in terms of reliability, accuracy and speed. Furthermore, a tracking system able to revert back EIS in controlled environments is designed. This allows one to investigate the differences between the existing EIS implementations. The additional knowledge thus acquired can be exploited and integrated in order to design and implement better future PRNU pattern alignment methods, aware of EIS and suitable for video source identification in multimedia forensics applications.

Bellavia F., Fanfani M., Colombo C., & Piva A. (2021). Experiencing with electronic image stabilization and PRNU through scene content image registration. PATTERN RECOGNITION LETTERS, 145, 8-15 [10.1016/j.patrec.2021.01.014].

Experiencing with electronic image stabilization and PRNU through scene content image registration

Bellavia F.;
2021

Abstract

This paper explores content-based image registration as a means of dealing with and understanding better Electronic Image Stabilization (EIS) in the context of Photo Response Non-Uniformity (PRNU) alignment. A novel and robust solution to extrapolate the transformation relating the different image output formats for a given device model is proposed. This general approach can be adapted to specifically extract the scale factor (and, when appropriate, the translation) so as to align native resolution images to video frames, with or without EIS on, and proceed to compare PRNU patterns. Comparative evaluations show that the proposed approach outperforms those based on brute-force and particle swarm optimization in terms of reliability, accuracy and speed. Furthermore, a tracking system able to revert back EIS in controlled environments is designed. This allows one to investigate the differences between the existing EIS implementations. The additional knowledge thus acquired can be exploited and integrated in order to design and implement better future PRNU pattern alignment methods, aware of EIS and suitable for video source identification in multimedia forensics applications.
Settore INF/01 - Informatica
Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni
https://www.sciencedirect.com/science/article/pii/S0167865521000271?via=ihub
Bellavia F., Fanfani M., Colombo C., & Piva A. (2021). Experiencing with electronic image stabilization and PRNU through scene content image registration. PATTERN RECOGNITION LETTERS, 145, 8-15 [10.1016/j.patrec.2021.01.014].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/480767
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