  Source camera identification tools assist image forensic investigators to
associate an image in question with a suspect camera. Various techniques have
been developed based on the analysis of the subtle traces left in the images
during the acquisition. The Photo Response Non Uniformity (PRNU) noise pattern
caused by sensor imperfections has been proven to be an effective way to
identify the source camera. The existing literature suggests that the PRNU is
the only fingerprint that is device-specific and capable of identifying the
exact source device. However, the PRNU is susceptible to camera settings, image
content, image processing operations, and counter-forensic attacks. A forensic
investigator unaware of counter-forensic attacks or incidental image
manipulations is at the risk of getting misled. The spatial synchronization
requirement during the matching of two PRNUs also represents a major limitation
of the PRNU. In recent years, deep learning based approaches have been
successful in identifying source camera models. However, the identification of
individual cameras of the same model through these data-driven approaches
remains unsatisfactory. In this paper, we bring to light the existence of a new
robust data-driven device-specific fingerprint in digital images which is
capable of identifying the individual cameras of the same model. It is
discovered that the new device fingerprint is location-independent, stochastic,
and globally available, which resolve the spatial synchronization issue. Unlike
the PRNU, which resides in the high-frequency band, the new device fingerprint
is extracted from the low and mid-frequency bands, which resolves the fragility
issue that the PRNU is unable to contend with. Our experiments on various
datasets demonstrate that the new fingerprint is highly resilient to image
manipulations such as rotation, gamma correction, and aggressive JPEG
compression.
