Authentication in Liveness Detection Utilizing CNN and MobileViT Algorithm
Bibliographic record
Abstract
Liveness detection is a critical component in biometric security systems, aiming to distinguish between live and spoofed biometric samples to ensure system authentication.Image technology advancements are one of the factors that lead to attacks on liveness detection.The use of camera and mask, have made it less difficult to generate attacks that target the liveness detection system, including deep fake, replay, and print attacks.A reliable approach is required to more accurately identify these attacks.Recent advances in deep learning have shown significant promise in addressing these challenges by learning robust and adaptive features directly from raw biometric data.This paper provides an experimental research of deep learning approaches for liveness detection, focusing on Convolutional Neural Networks (CNNs) including EfficientNetV2S, EfficientNetV2M, EfficientNetV2L, and comparing with MobileViT for facial recognition in liveness detection.The datasets employed are NUAA, Synthetic, and iBeta 1.This paper examines the strengths and limitations of each method, and evaluation metrics used in the field, and highlight the latest breakthroughs in improving detection accuracy and robustness against diverse replay and print attacks.Experimental results show that EfficientNetV2S outperforms other algorithms, both in terms of accuracy and false detection rate.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".