  Lens aberrations have previously been used to determine the provenance of an
image. However, this is not necessarily unique to an image sensor, as lens
systems are often interchanged. Photo-response non-uniformity noise was
proposed in 2005 by Luk\'a\v{s}, Goljan and Fridrich as a stochastic signal
which describes a sensor uniquely, akin to a "ballistic" fingerprint. This
method, however, did not account for additional sources of bias such as lens
artefacts and temperature.
  In this paper, we propose a new additive signal model to account for
artefacts previously thought to have been isolated from the ballistic
fingerprint. Our proposed model separates sensor level artefacts from the lens
optical system and thus accounts for lens aberrations previously thought to be
filtered out. Specifically, we apply standard image processing theory, an
understanding of frequency properties relating to the physics of light and
temperature response of sensor dark current to classify artefacts. This model
enables us to isolate and account for bias from the lens optical system and
temperature within the current model.
