Enhancing Visual Speaker Authentication using Dynamic Lip Movement and Meta-Learning
Bibliographic record
Abstract
Visual speaker authentication (VSA) remains vulnerable to sophisticated spoofing methods, such as deepfakes. Traditional deep learning approaches require extensive userspecific enrollment data and show poor generalization to new speakers. In response, we employ a few-shot meta-learning technique, specifically Model-Agnostic Meta-Learning (MAML), integrated with dynamic lip movement analysis utilizing optical flow to develop a scalable anti-spoof VSA framework. We validated our model using the GRID audiovisual dataset, with spoofing attacks simulated via Wav2Lip for deepfake lip synchronization. The results demonstrate the model’s superior performance, evidenced by near-perfect classification accuracy and negligible error rates, addressing two key challenges of VSA: eliminating extensive training data while enabling rapid adaptation to unfamiliar speakers. Our approach significantly surpasses standard non-meta-learning approaches, substantiating its ability to address real-world scenarios.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".