Biblio

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%.

Despite the recent introduction of biometric identification technology, Personal Identification Numbers (PIN) are the standard for granting access to restricted areas and for authorizing operations on most systems, including mobile phones, payment devices, smart locks. Unfortunately, PINs have several inherent vulnerabilities and expose users to different types of social engineering attacks. Specifically, the risk of shoulder surfing in PIN-based authentication is especially high for individuals who are blind. In this paper, we introduce a new method for improving the trade-off between security and accessibility in PIN-based authentication systems. Our proposed solution aims at minimizing the threats posed by malicious agents while maintaining a low level of complexity for the user. We present the method and discuss the results of an evaluation study that demonstrates the advantages of our solution compared to state-of-the-art systems.

Currently, usable security and web accessibility design principles exist separately. Although literature at the intersect of accessibility and security is developing, it is limited in its understanding of how users with vision loss operate the web securely. In this paper, we propose heuristics that fuse the nuances of both fields. With these heuristics, we evaluate 10 websites and uncover several issues that can impede users' ability to abide by common security advice.