Guessing click-based graphical passwords by eye tracking
Bibliographic record
Abstract
Click-based graphical passwords are a new method of authentication where passwords are created and entered by clicking in particular places on an image. This paper presents a study that investigated eye tracking as a potential threat to the security of such passwords. If the gaze data from people looking at an image resembles the click-points of other people's passwords, then covert eye tracking might be used to create dictionaries to effectively guess passwords. The study used an eye tracker to record the participants' gaze as they looked at images that had been used as the basis for passwords in an earlier study. We then compared the eye tracker data with the actual password click-points gathered during the earlier study, and conducted several forms of analysis to determine the likely success of guessing passwords. The eye tracker data did somewhat resemble the password click-points, and might offer attackers an advantage over guessing at random. The effectiveness shown for this approach was limited, however, although might allow improvement that would result in greater danger, especially if gaze data could be gathered without explicit interaction.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".