Proactive Audio Authentication Using Speaker Identity Watermarking
Bibliographic record
Abstract
Generative AI, particularly through “deep fake” technology, stands at the crossroads of innovation and ethical dilemma. On one hand, it brings unprecedented advancements, transforming how we interact with digital content. On the other hand, it significantly compromises privacy and security, casting a shadow over the reliability of speaker recognition systems and fueling misuse in telecommunication fraud and manipulation of public opinion. This stark contrast not only raises legitimate concerns over the safety of sharing personal audio and video but also questions the very authenticity of digital media. To address the challenges of traceability in deepfake content and guarantee the integrity of audio, we propose a new solution specifically designed to counteract voice conversion and synthetic speech attacks. Leveraging cutting-edge deep learning technology, three extension strategies and ensemble learning of synthesis layer, this approach not only overcomes the inherent limitations of existing forensic methods but also resolves the issues associated with high-capacity watermarks. It achieves exceptionally high accuracy and imperceptibility across multiple speech datasets, various synthetic forgery methods, and numerous speech processing algorithms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".