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PRNU-based image processing is a key asset in digital multimedia forensics.It allows for reliable device identification and effective detection andlocalization of image forgeries, in very general conditions. However,performance impairs significantly in challenging conditions involving lowquality and quantity of data. These include working on compressed and croppedimages, or estimating the camera PRNU pattern based on only a few images. Toboost the performance of PRNU-based analyses in such conditions we propose toleverage the image noiseprint, a recently proposed camera-model fingerprintthat has proved effective for several forensic tasks. Numerical experiments ondatasets widely used for source identification prove that the proposed methodensures a significant performance improvement in a wide range of challengingsituations.

Combining PRNU and noiseprint for robust and efficient device source identification