  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.
