We tested the latest implementation (Build 8782) of PRNU-based Camera Identification and Tampering Localization on a “base dataset” of 10.069 images, coming from 29 devices (listed in the table below). We split the dataset in two:
– Reference set: 1450 images (50 per device) were used for CRP estimation
– Test set: 8619 images were used for testing. On average, each device was tested against approximately 150 matching images and approximately 150 non-matching images.
It is important to understand that, in most cases, we could not control the image creation process. This means that images may have been captured using digital zoom or at resolutions different than the default one, which makes PRNU analysis ineffective. Making use of EXIF metadata, we could filter out these images from the Reference set. However, we chose not to filter out such images from the Test set: we prefer showing results that are closer to real-world cases, rather than tricking the dataset to obtain 100% performance.
Using the above base dataset, we carried out several experiments:
– Experiment 1) testing the system on images “as they are”
– Experiment 2) camera identification in presence or rotation, resize and JPEG re-compression
– Experiment 3) camera identification in presence of cropping, rotation and JPEG re-compression
– Experiment 4) discriminating devices of the same model
– Experiment 5) investigating the impact of the number of images used for CRP computation.