  Crime in the 21st century is split into a virtual and real world. However,
the former has become a global menace to people's well-being and security in
the latter. The challenges it presents must be faced with unified global
cooperation, and we must rely more than ever on automated yet trustworthy tools
to combat the ever-growing nature of online offenses. Over 10 million child
sexual abuse reports are submitted to the US National Center for Missing &
Exploited Children every year, and over 80% originated from online sources.
Therefore, investigation centers and clearinghouses cannot manually process and
correctly investigate all imagery. In light of that, reliable automated tools
that can securely and efficiently deal with this data are paramount. In this
sense, the scene recognition task looks for contextual cues in the environment,
being able to group and classify child sexual abuse data without requiring to
be trained on sensitive material. The scarcity and limitations of working with
child sexual abuse images lead to self-supervised learning, a machine-learning
methodology that leverages unlabeled data to produce powerful representations
that can be more easily transferred to target tasks. This work shows that
self-supervised deep learning models pre-trained on scene-centric data can
reach 71.6% balanced accuracy on our indoor scene classification task and, on
average, 2.2 percentage points better performance than a fully supervised
version. We cooperate with Brazilian Federal Police experts to evaluate our
indoor classification model on actual child abuse material. The results
demonstrate a notable discrepancy between the features observed in widely used
scene datasets and those depicted on sensitive materials.
