  Photo Response Non-Uniformity (PRNU) is considered the most effective trace
for the image source attribution task. Its uniqueness ensures that the sensor
pattern noises extracted from different cameras are strongly uncorrelated, even
when they belong to the same camera model. However, with the advent of
computational photography, most recent devices heavily process the acquired
pixels, possibly introducing non-unique artifacts that may reduce PRNU noise's
distinctiveness, especially when several exemplars of the same device model are
involved in the analysis. Considering that PRNU is an image forensic technology
that finds actual and wide use by law enforcement agencies worldwide, it is
essential to keep validating such technology on recent devices as they appear.
In this paper, we perform an extensive testing campaign on over 33.000 Flickr
images belonging to 45 smartphone and 25 DSLR camera models released recently
to determine how widespread the issue is and which is the plausible cause.
Experiments highlight that most brands, like Samsung, Huawei, Canon, Nikon,
Fujifilm, Sigma, and Leica, are strongly affected by this issue. We show that
the primary cause of high false alarm rates cannot be directly related to
specific camera models, firmware, nor image contents. It is evident that the
effectiveness of \prnu based source identification on the most recent devices
must be reconsidered in light of these results. Therefore, this paper is
intended as a call to action for the scientific community rather than a
complete treatment of the subject. Moreover, we believe publishing these data
is important to raise awareness about a possible issue with PRNU reliability in
the law enforcement world.
