  With 3.78 billion social media users worldwide in 2021 (48% of the human
population), almost 3 billion images are shared daily. At the same time, a
consistent evolution of smartphone cameras has led to a photography explosion
with 85% of all new pictures being captured using smartphones. However, lately,
there has been an increased discussion of privacy concerns when a person being
photographed is unaware of the picture being taken or has reservations about
the same being shared. These privacy violations are amplified for people with
disabilities, who may find it challenging to raise dissent even if they are
aware. Such unauthorized image captures may also be misused to gain sympathy by
third-party organizations, leading to a privacy breach. Privacy for people with
disabilities has so far received comparatively less attention from the AI
community. This motivates us to work towards a solution to generate
privacy-conscious cues for raising awareness in smartphone users of any
sensitivity in their viewfinder content. To this end, we introduce PrivPAS (A
real time Privacy-Preserving AI System) a novel framework to identify sensitive
content. Additionally, we curate and annotate a dataset to identify and
localize accessibility markers and classify whether an image is sensitive to a
featured subject with a disability. We demonstrate that the proposed
lightweight architecture, with a memory footprint of a mere 8.49MB, achieves a
high mAP of 89.52% on resource-constrained devices. Furthermore, our pipeline,
trained on face anonymized data, achieves an F1-score of 73.1%.
