Source camera linking using enhanced sensor pattern noise extracted from images

Source camera linking, i.e., establishing whether or not the images of interest are taken by the same camera without the camera and its digital fingerprint in the investigator's possession, is an important aspect of image forensics. Sensor pattern noises (SPNs), extracted from digital images as device fingerprints, have been proved as an effective way for digital device identification and have been used for device linking as well. However, as we demonstrate in this work, the limitation of the current method of extracting the sensor pattern noise is that the SPNs extracted from images can be severely contaminated by the details from scenes. This makes device linking a more challenging problem than source device identification because the absence of the camera prohibits the acquisition of a clean fingerprint of the camera. In this work we propose a novel approach for attenuating the influence of the details from the scenes on sensor pattern noises so as to improve the correct rate of device linking. The hypothesis underlying our SPN enhancement method is that the stronger a signal component in a SPN is, the less trustworthy the component should be and thus should be attenuated. This hypothesis suggests that an enhanced SPN can be obtained by assigning weighting factors inversely proportional to the magnitude of the SPN components. (6 pages)