Name
Abstract | ||
|---|---|---|
Photo Response Non-Uniformity (PRNU) is the defacto standard in image source identification, allowing scientists, researchers, forensics investigators and courts to bind a picture under investigation to the specific camera sensor that took the shot at first place. Caused by silicon sensor imperfections, PRNU is characterized as a Gaussian i.i.d weak multiplicative noise embedded into every digital photo at acquisition time. Despite PRNU nearly-flat spectral characteristics, it undergoes several interpolations steps while image is demosaicked and optionally JPEG compressed. In this paper we propose a novel approach to the design of projection matrices tailored to PRNU compression. Joint effect of interpolation and projection on cross-correlation test is first analyzed, in order to derive those conditions that maximize detection while reducing false-alarm probability. A design methodology to build effective projection matrices is then presented, taking into account computational complexity. Validation of the proposed approach is finally performed against state-of-the-art methods on a well known public image dataset. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/WIFS.2017.8267652 | 2017 IEEE Workshop on Information Forensics and Security (WIFS) |
Keywords | Field | DocType |
digital photo,acquisition time,nearly-flat spectral characteristics,PRNU compression,interpolation,design methodology,defacto standard,image source identification,forensics investigators,specific camera sensor,silicon sensor imperfections,weak multiplicative noise,photo response nonuniformity,projection matrices,public image dataset | Computer vision,Digital photography,Image sensor,Computer science,Interpolation,Signal-to-noise ratio,Gaussian,JPEG,Artificial intelligence,Multiplicative noise,Computational complexity theory | Conference |
ISSN | ISBN | Citations |
2157-4766 | 978-1-5090-6770-1 | 1 |
PageRank | References | Authors |
0.36 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luca Bondi | 1 | 165 | 11.04 |
| Fernando Pérez-González | 2 | 727 | 93.38 |
| Paolo Bestagini | 3 | 261 | 32.01 |
| Stefano Tubaro | 4 | 1033 | 119.50 |