A Comparative Study on Physical and Perceptual Features for Deepfake Audio Detection
Bibliographic record
Abstract
Audio content synthesis has stepped into a new era and brought a great threat to daily life since the development of deep learning techniques. The ASVSpoof Challenge and the ADD Challenge have been launched to motivate the development of Deepfake audio detection algorithms. Currently, the detection models, which consist of front-end feature extractors and back-end classifiers, utilize the physical features mainly, rather than the perceptual features that relate to natural emotions or breathiness. Therefore, we provide a comprehensive study on 16 physical and perceptual features and evaluate their effectiveness in both Track 1 and Track 2 of the ADD Challenge. Based on results, PLP, as a perceptual feature, outperforms the rest of the features in Track 1, while CQCC has the best performance in Track 2. Our experiments demonstrate the significance of perceptual features in detecting Deepfake audios. We also seek to explore the underlying characteristics of features that can distinguish Deepfake audio from a real one. We perform statistical analysis on each feature to show its distribution differences on real and synthesized audios. This paper will provide a potential direction in selecting appropriate feature extraction methods for the future implementation of detection models.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".