Exploring usability effects of increasing security in click-based graphical passwords
Bibliographic record
Abstract
Graphical passwords have been proposed to address known problems with traditional text passwords. For example, memorable user-chosen text passwords are predictable, but random system-assigned passwords are difficult to remember. We explore the usability effects of modifying system parameters to increase the security of a click-based graphical password system. Generally, usability tests for graphical passwords have used configurations resulting in password spaces smaller than that of common text passwords. Our two-part lab study compares the effects of varying the number of click-points and the image size, including when different configurations provide comparable password spaces. For comparable spaces, no usability advantage was evident between more click-points, or a larger image. This is contrary to our expectation that larger image size (with fewer click-points) might offer usability advantages over more click-points (with correspondingly smaller images). The results suggest promising opportunities for better matching graphical password system configurations to device constraints, or capabilities of individual users, without degrading usability. For example, more click-points could be used on smart-phone displays where larger image sizes are not possible.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".