Shoulder Surf Resistant Screen Locking for Smartphones: A Review of Fifty Non-Biometric Methods

Aris, Hazleen; Yaakob, Wira Firdaus

doi:10.1109/ains.2018.8631419

Cited by 1 publication

(2 citation statements)

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“…To address this concern, various countermeasures have been proposed, such as increasing the complexity of patterns or obscuring part of the pattern using external hardware. However, a 2018 systematic literature review [4] found that of the 84 countermeasures proposed for pattern locking on smartphones. Only 10 pattern-based locking schemes were known, and the majority of them emphasized increasing pattern complexity.…”

Section: Introductionmentioning

confidence: 99%

“…This study intended to establish a new pattern password mechanism, called Hollow-Pass, by combining the View manipulation and Image degradation techniques as described in Aris and Yaakob's review of anti-shoulder-surfing techniques [4]. The efficacy of Hollow-Pass in terms of both usability and security was also assessed through a limited-scale user experiment.…”

Section: Introductionmentioning

confidence: 99%

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Hollow-Pass: A Dual-View Pattern Password Against Shoulder-Surfing Attacks

Tan

Sarmah

2023

Lecture Notes in Computer Science

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This paper presents Hollow-Pass, a developed solution that strengthens the security of pattern passwords against shoulder-surfing attacks. It is a novel approach to graphical password (GP) schemes that utilize a dual-view technology known as the global precedence effect, which eliminates the need for external devices and makes the grid and pattern invisible to potential shoulder surfers. The usability of Hollow-Pass was evaluated through an online as well as an offline user test. We recruited 30 participants from varied backgrounds, ranging in age from 20 to 80 years, for the online user test. An offline small-scale sampling test was conducted among 19 undergraduates from the Universiteit of Twente. The developed solution successfully demonstrated its ability to effectively resist shoulder-surfing attacks for simple patterns at various viewing angles (front, left-front, and right-front) and different distances (1.0 m, 1.5 m, and 2.0 m).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%