Combining PRNU and noiseprint for robust and efficient device source identification
Abstract
PRNU-based image processing is a key asset in digital multimedia forensics. It allows for reliable device identification and effective detection and localization of image forgeries, in very general conditions. However, performance impairs significantly in challenging conditions involving low quality and quantity of data. These include working on compressed and cropped images or estimating the camera PRNU pattern based on only a few images. To boost the performance of PRNU-based analyses in such conditions, we propose to leverage the image noiseprint, a recently proposed camera-model fingerprint that has proved effective for several forensic tasks. Numerical experiments on datasets widely used for source identification prove that the proposed method ensures a significant performance improvement in a wide range of challenging situations.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 12, 2020
- Source ID
- 10.1186/s13635-020-0101-7
Entities
People
- Davide Cozzolino
- Diego Gragnaniello
- Francesco Marra
- Giovanni Poggi
- Luisa Verdoliva
Organizations
- Defense Advanced Research Projects Agency