Gesture and Sociability-based Continuous Authentication on Smart Mobile Devices
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
In this paper, we propose a new continuous verification platform on smart mobile devices. To this end, we integrate gesture-based features with interaction with social networking apps to verify user identities without minimum requirement for a password, pin code or biometric means. The continuous verification subsystem of this work proposes a novel two-step system for verification of users. The subsystem works by having two accurate models working as a primary and backup; when the primary fails the backup takes over to confirm or deny the conclusion of the primary model. The false acceptance rate (FAR) and false rejection rate (FRR) achieved under the proposed two-step system are shown to be 2.54% and 1.98% respectively, compared to the FAR and FRR of single-step verification, which achieved 3.15% and 9.13% respectively. Furthermore, the proposed system also improves the stability of continuous verification. In this work we show that the single step systems are inconsistent when analyzing small feature sets or slightly varied datasets. During both of these instances, the proposed system stays consistent, maintaining a high verification rate.
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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 | 0.000 |
| 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