Bibliographic record
Abstract
Computer programs are misusing Internet services designed for humans. A CAPTCHA, Completely Automated Public Turing test to tell Computers and Humans Apart, is a standard security mechanism to defend against such attacks. Two fundamental issues with CAPTCHAs are usability and robustness. It is important for a CAPTCHA to be both legible for humans and strong against malicious computer programs. Recently, computer vision and pattern recognition algorithms have broken many well-known CAPTCHAs. Lack of security and usability in CAPTCHAs designed to protect popular websites such as Gmail and Yahoo mail, with almost 500 million users in July 2011, would cause huge problems. Therefore, security researchers have become motivated to discover techniques to improve CAPTCHAs. Exploiting the gap in the recognition abilities between humans and computers is a key point to design a CAPTCHA that is hard-to-break for machines but easy-to-solve for humans. In this paper, we introduce current CAPTCHAs and attacks against them; we investigate the robustness and usability of current CAPTCHAs and discuss ideas to develop more robust and usable CAPTCHAs.
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.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".