Tap Based User Authentication on Smartphones for Visually Impaired People

Banerjee, Amit; Hasan, Mahamudul

doi:10.1109/icccnt.2018.8494056

Cited by 3 publications

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Banerjee et al proposed a system using tapping on the grid-based PIN entry in smartphones. 17 Balaji et al proposed an approach inspired by the braille as persons with visual impairments are well aware of the braille language. 18 Azad et al Proposed a system combining the PassPoints and Press Touch Code to form a hybrid PIN entry model.…”

Section: Related Workmentioning

confidence: 99%

ARJUNA: An accessible pin entry model in smartphones for persons with low vision

Balayogi,

Kuppusamy

2023

Internet Technology Letters

View full text Add to dashboard Cite

User authentication is the cornerstone of providing secure access to the services in the digital ecosystem. Critical applications such as Banking use Personal Identification Number (PIN)‐based passwords and One Time Passwords (OTP), for transactions to strengthen security. However, persons with visual impairments have often been a soft target for cyber attacks as it is comparatively simpler to make them victims of various attacks. This paper proposes a model titled Accessible Robust Just‐in‐time Numerical Authentication (ARJUNA) to assist PIN entry in smartphones for people with visual impairments. This study has explored several cyber‐attacks on visually impaired users and proposed the ARJUNA model that provides a robust braille‐inspired PIN input for people with visual impairments. The proposed model is implemented as an Android application that users can easily access. Several experiments are conducted with visually impaired users to study the model's performance. The proposed ARJUNA model has shown supportive results in the user study. The robustness of ARJUNA against various attacks is also explored in this article.

show abstract

Section: Related Workmentioning

confidence: 99%