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Record W3164170294 · doi:10.1145/3448018.3458615

Eye-GUAna: Higher Gaze-Based Entropy and Increased Password Space in Graphical User Authentication Through Gamification

2021· article· en· W3164170294 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACM Symposium on Eye Tracking Research and Applications · 2021
Typearticle
Languageen
FieldComputer Science
TopicUser Authentication and Security Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPasswordComputer scienceGazeHuman–computer interactionEye trackingCognitive passwordAuthentication (law)Entropy (arrow of time)Process (computing)Artificial intelligenceComputer securityPassword strengthOne-time password

Abstract

fetched live from OpenAlex

Graphical user authentication (GUA) is a common alternative to text-based user authentication, where people are required to draw graphical passwords on background images. Recent research provides evidence that gamification of the graphical password creation process influences people to make less predictable choices. Aiming to understand the underlying reasons from a visual behavior perspective, in this paper, we report a small-scale eye-tracking study that compares the visual behavior developed by people who follow a gamified approach and people who follow a non-gamified approach to make their graphical password choices. The results show that people who follow a gamified approach have higher gaze-based entropy, as they fixate on more image areas and for longer periods, and thus, they have an increased effective password space, which could lead to better and less predictable password choices.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.719
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.048
GPT teacher head0.353
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it