Lightweight and Secure Face-based Active Authentication for Mobile Users
Bibliographic record
Abstract
Active Authentication (AA) systems continuously authenticate users on smartphones. With high quality front-facing cameras available on recent smartphones, face-based active authentication emerges as a good candidate for AA systems. On the other hand, secure authentication of mobile users is a big concern in biometric systems. Mobile match-on-card (MMOC) technique takes advantage of SIM/eSIM card as a secure element (SE) to protect biometric templates and verify users isolated from the smartphone's environment. However, resource limitations of smart cards make MMOC authentication hard to implement. In this paper, we propose two system architectures for MMOC face-based AA systems. In Cloud-assisted MMOC architecture (CA-MMOC), we use cloud resources for model selection and training. Full MMOC architecture (F-MMOC) relies only on SIM/eSIM card's resources for enrollment and verification. A quantization scheme is proposed to make the authentication system implementable on SIM cards, plus a speed-up technique to reduce on-card execution time. Using a public mobile video dataset, we evaluate the proposed system. Our evaluation results show that the proposed MMOC authentication achieves high accuracy in real-time with a small memory footprint on SIM, and is suitable for cross-platform authentication. We also implement the CA-MMOC system on a real smartphone and evaluate the system's performance overhead in terms of power consumption, CPU and memory usage.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".