Mobile Match on Card Active Authentication Using Touchscreen Biometric
Bibliographic record
Abstract
With the wide use of personal consumer electronics devices such as smartphones, people store sensitive and confidential information more on their devices. Active authentication (AA) systems continuously authenticate users to reduce possible attacks after a successful login on the device. In this article, we propose match-on-card (MOC) approach for a secure active authentication scheme using touchscreen for smartphones to enhance the security and privacy and decrease the performance overhead on the consumer device. We train a Deep Neural Network (DNN) model, and store the model on the smart card available on the device for user authentication. To implement the user verification on smart cards, we quantize inputs to the model and the model's parameters. A speed-up technique is added to the verification phase to improve the execution time. Evaluation results show that with a well configured DNN model, our on-card authentication reaches an Equal Error Rate (EER) of 2.6% for 15 strokes and verification time of 0.65 second for each stroke. Considering the average user's stroke frequency of 1 stroke/s, our proposed scheme shows the potential for mobile MOC active authentication using touchscreen gestures on consumer devices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".