Dynamic Mirrored CAPTCHA Design and Security Evaluation
Bibliographic record
Abstract
Completely Automated Public Turing test to Tell Computers and Humans Apart (CAPTCHA) is generally helpful in endorsing that it is human being interacting with the web services but not any attack by a bot.These are used to differentiate between humans and computers, preventing bots from automated operations such as spamming, scraping content.With the advancements of Artificial Intelligence, CAPTCHAs are facing problems because of its ability to bypass captcha using Optical Character Recognizer (OCR) technology.The primary objective of this research is to propose a novel robust CAPTCHA design that can withstand the attacks of bots.It is accomplished by understanding the loopholes of the current CAPTCHA system.This work suggests ways to improve security by implementing CAPTCHA using dynamically positioned rotating inverted alphabet characters, thereby safeguarding internet services from automated bot attacks.The results shown 93% of times OCR failed to correctly identify all the characters of generated CAPTCHA and around 87.5% of participants successfully recognized the characters in CAPTCHA.The project helps the United Nations Sustainable Development Goals (SDGs) to fulfil Goal 9 (Industry, Innovation, and Infrastructure).
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".