Robust Audio Anti-Spoofing System Based on Low-Frequency Sub-Band Information
Bibliographic record
Abstract
The current audio anti-spoofing systems usually have a computationally complex architecture without providing the fundamental discriminative factors for the detection judgments. The state-of-the-arts also highly depend on voice information to develop detector systems, which may become vulnerable when the spoofing algorithms have further improved the quality of fake speech. Therefore, we conduct a series of experiments on different frequency sub-bands to investigate the underlying discriminative features. We find the lowest frequency sub-band in the range from 0 to 1600Hz contains the most critical features that distinguish between Deepfake and real speech. We also focus on forensic evidence and identify that the basis of detectors’ judgment exists in non-speech parts in audio samples. Based on the findings, our single detection system, with only 57K parameters and utilizing a one-tenth segment of the entire spectrogram as input, demonstrates its robustness by outperforming all official baselines of the ASVspoof2021 DF track. Our lightweight system can be easily applied in practical use cases, such as automated Deepfake screening or protecting voice-able devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.004 |
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".