  Sensor pattern noise has been found to be a reliable tool for providing
information relating to the provenance of an image. Conventionally sensor
pattern noise is modelled as a mutual interaction of pixel non-uniformity noise
and dark current. By using a wavelet denoising filter it is possible to isolate
a unique signal within a sensor caused by the way the silicon reacts
non-uniformly to light. This signal is often referred to as a fingerprint. To
obtain the estimate of this photo response non-uniformity multiple sample
images are averaged and filtered to derive a noise residue. This process and
model, while useful at providing insight into an images provenance, fails to
take into account additional sources of noise that are obtained during this
process. These other sources of noise include digital processing artefacts
collectively known as camera noise, image compression artefacts, lens
artefacts, and image content. By analysing the diversity of sources of noise
remaining within the noise residue, we show that further insight is possible
within a unified sensor pattern noise concept which opens the field to
approaches for obtaining fingerprints utilising fewer resources with comparable
performance to existing methods.
