PrivPAS: A real time Privacy-Preserving AI System and applied ethics

TL;DR

PrivPAS is a real-time AI system designed to detect sensitive content in images, especially for individuals with disabilities, raising awareness and preserving privacy on resource-limited devices.

Contribution

We introduce PrivPAS, a lightweight, real-time AI framework for identifying sensitive visual content, with a curated dataset and high accuracy on resource-constrained devices.

Findings

Achieves 89.52% mAP on sensitive content detection.

Memory footprint of only 8.49MB for resource-limited devices.

F1-score of 73.1% on face-anonymized sensitive content classification.

Abstract

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%.