On the Reliability of the PNU for Source Camera Identification Tasks

The PNU is an essential and reliable tool to perform SCI and, during the years, became a standard de-facto for this task in the forensic field. In this paper, we show that, although strategies exist that aim to cancel, modify, replace the PNU traces in a digital camera image, it is still possible, through our experimental method, to find residual traces of the noise produced by the sensor used to shoot the photo. Furthermore, we show that is possible to inject the PNU of a different camera in a target image and trace it back to the source camera, but only under the condition that the new camera is of the same model of the original one used to take the target image. Both cameras must fall within our availability. For completeness, we carried out 2 experiments and, rather than using the popular public reference dataset, CASIA TIDE, we preferred to introduce a dataset that does not present any kind of statistical artifacts. A preliminary experiment on a small dataset of smartphones showed that the injection of PNU from a different device makes it impossible to identify the source camera correctly. For a second experiment, we built a large dataset of images taken with the same model DSLR. We extracted a denoised version of each image, injected each one with the RN of all the cameras in the dataset and compared all with a RP from each camera. The results of the experiments, clearly, show that either in the denoised images and the injected ones is possible to find residual traces of the original camera PNU. The combined results of the experiments show that, even in theory is possible to remove or replace the \ac{PNU} from an image, this process can be, easily, detected and is possible, under some hard conditions, confirming the robustness of the \ac{PNU} under this type of attacks.