Mel-Spectrogram Image-Based End-to-End Audio Deepfake Detection Under Channel-Mismatched Conditions
Bibliographic record
Abstract
This work focuses on the problem of detecting fake audio clips. To improve current audio spoofing detection models, we propose a selection of multiple audio augmentations spe-cially designed to resemble audio spoofing attacks. These augmentations are experimentally found to be very useful and using them achieves a notable performance of 2.8% EER on the ASVspoof 2019 challenge evaluation set. Unlike the widely employed acoustic features, in this paper we explore the use of Mel-spectrogram image features and employ vari-ous audio codecs to achieve robustness to codec and transmission channel variability present in the ASVspoof2021 Evalu-ation set. To better handle spectral information, crucial to de-tect spoofing, we adopt the WaveletCNN and VGG16 archi-tectures which outperform all baselines. Finally, we find that robustness of countermeasure systems degrades dramatically when provided with speech samples degraded through VoIP network transmission or mismatching audio compression.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.004 | 0.003 |
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".