Development of an Efficient ECG and PPG Signal Processing-based Spoof Detection System using Convolutional Neural Networks
Bibliographic record
Abstract
Biometric authentication systems have a large range of applications in the real-life situations. These systems receive various types of signals from the biometric sensors and process them efficiently in order to determine their authenticity. However, these systems are often vulnerable to the biometric spoof signals that are synthetically generated. In view of this, the design of the efficient spoof detection methods for the biometric authentication systems is of paramount importance. There exist various biological signal processing-based biometric authentication systems that use the combination of electrocardiogram (ECG) and photoplethysmogram (PPG) signals for performing their operations. In this paper, we propose a novel spoof detection method for the ECG and PPG signal processing-based biometric authentication systems that is able to reliably detect the spoof signals from the authentic ones. Specifically, the proposed method utilizes two stages, which the first stage involves in obtaining the authenticity status of the ECG and PPG signals separately using the convolutional neural networks and the Bayes classifier. In the second stage of the proposed scheme, the spectro-temporal features of the ECG and PPG signals are extracted in order to further enhance the performance of the task of spoof detection. It is shown that our spoof detection system is able to provide a high performance on the benchmark dataset of the biometric applications.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".