Enhancing CAPTCHA Security Using Interactivity, Dynamism, and Mouse Movement Patterns
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.
Bibliographic record
Abstract
Many existing CAPTCHAs require users to identify characters in a static image and match them with their counterparts in another image. Requiring intelligent human interaction in the matching task of these CAPTCHAs will pose a second challenge, which is straightforward for human users but difficult to emulate for Bots. In this paper, the authors develop several interactive matching tasks involving dynamic elements and demonstrate their impact on CAPTCHA security and usability in a series of tests and user studies. Their tests indicate that requiring intelligent human interaction can substantially decrease the likelihood of a CAPTCHA being broken in addition to making an attack computationally expensive. The authors' results provide both a security and a usability benchmark for the development of interactive dual-challenge CAPTCHAs. Their proposed findings from users' mouse movement data analysis can be readily incorporated in several types of existing CAPTCHA to enhance their security.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it