Passnumbers: An Approach of Graphical Password Authentication Based on Grid Selection
Bibliographic record
Abstract
The authentication textual passwords are the most widely used technique. However, this type of legacy authentication is vulnerable to various attacks, such as shoulder-surfing attacks. Hence, graphical password authentication is one of these approaches which has been suggested to overcome the issues related to textual passwords. Nevertheless, the hackers have also developed new techniques that can be finally broken the graphical password, for instance, listening to the transmitted information between the client and the server. In this paper, Passnumbers graphical authentication password is proposed. Passnumbers approach involves two new stages, which are first, using the coordinates of a graphical grid cells-based numbers for entering the password. The second stage is represented by deploying a new technique to encrypt the password based on the image pixels. The performance evaluation reveals that the proposed Passnumbers can provide high resistance against several graphical password attacks including shoulder surfing and eavesdropping attacks. Passnumbers is evaluated using several metrics including security and usability.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".