Implicit Identity Authentication Method Based on User Posture Perception
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
Smart terminals use passwords and physiological characteristics such as fingerprints to authenticate users. Traditional authentication methods work when users unlock their phones, but they cannot continuously verify the user’s legal identity. Therefore, the one-time authentication implemented by conventional authentication methods cannot meet security requirements. Implicit authentication technology based on user behavior characteristics is proposed to achieve the continuous and uninterrupted authentication of savvy terminal users. This paper proposes an implicit authentication method that fuses keystroke and sensor data. To improve the accuracy of authentication, a neural network-based feature extraction model that integrates keystroke data and motion sensor data is designed. A feature space with dual-channel fusion is constructed, and a dataset collected in real scenarios is built by considering the changes in user activity scenarios and the differences in terminal holding postures. Experimental results on the collected data show that the proposed method has improved the accuracy of user authentication to a certain extent.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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