  With the advent of internet, not safe for work(NSFW) content moderation is a
major problem today. Since,smartphones are now part of daily life of billions
of people,it becomes even more important to have a solution which coulddetect
and suggest user about potential NSFW content present ontheir phone. In this
paper we present a novel on-device solutionfor detecting NSFW images. In
addition to conventional porno-graphic content moderation, we have also
included semi-nudecontent moderation as it is still NSFW in a large
demography.We have curated a dataset comprising of three major
categories,namely nude, semi-nude and safe images. We have created anensemble
of object detector and classifier for filtering of nudeand semi-nude contents.
The solution provides unsafe body partannotations along with identification of
semi-nude images. Weextensively tested our proposed solution on several public
datasetand also on our custom dataset. The model achieves F1 scoreof 0.91 with
95% precision and 88% recall on our customNSFW16k dataset and 0.92 MAP on NPDI
dataset. Moreover itachieves average 0.002 false positive rate on a collection
of safeimage open datasets.
