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arxiv: 2605.09087 · v1 · submitted 2026-05-09 · 💻 cs.SD · cs.LG

Recognition: 1 theorem link

· Lean Theorem

Towards Trustworthy Audio Deepfake Detection: A Systematic Framework for Diagnosing and Mitigating Gender Bias

Aishwarya Fursule, Anderson R. Avila, Shruti Kshirsagar

Pith reviewed 2026-05-12 02:42 UTC · model grok-4.3

classification 💻 cs.SD cs.LG
keywords audio deepfake detectiongender biasfairnessbias diagnosismitigationdecision thresholdASVSpoof5
0
0 comments X

The pith

Gender bias in audio deepfake detectors arises from acoustic representation differences, gender leakage in features, and evaluation asymmetry rather than data imbalance, and per-gender threshold adjustment reduces it by 54 to 75 percent.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a diagnosis-first framework for gender bias in audio deepfake detection systems. It determines that bias originates in how acoustic features are represented differently across genders, in gender information leaking into the learned model features, and in asymmetric ways of structuring evaluations, not from simply having unbalanced training data. This diagnosis guides the selection of mitigations. Adjusting decision thresholds separately for each gender cuts unfairness by 54 to 75 percent while keeping detection accuracy unchanged. A newly introduced epoch-level fairness regularisation method beats standard per-batch approaches, and adversarial debiasing works only when the diagnosis shows leakage is localised. No single technique eliminates the fairness gap entirely.

Core claim

The paper claims that bias sources must be diagnosed before mitigation because this step correctly predicts which fixes succeed: acoustic representation differences, gender leakage in learned features, and structural evaluation asymmetry are the identified causes rather than data imbalance; per-gender threshold adjustment reduces unfairness by 54 to 75 percent at no cost to accuracy; the new epoch-level fairness regularisation outperforms per-batch methods; and adversarial debiasing succeeds only when leakage is localised as the diagnosis forecasts. No single method closes the gap, so fairer benchmark design is also required.

What carries the argument

The diagnosis-first framework that first locates acoustic representation differences, gender leakage in learned features, and structural evaluation asymmetry before testing mitigations such as per-gender thresholding and epoch-level regularisation.

If this is right

  • Per-gender decision threshold adjustment reduces gender unfairness by 54 to 75 percent at no cost to detection accuracy.
  • Epoch-level fairness regularisation outperforms existing per-batch fairness methods.
  • Adversarial debiasing succeeds only when the diagnosis shows gender leakage is localised rather than diffuse.
  • Fairer benchmark design is required because no single mitigation fully closes the fairness gap.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same diagnosis-before-mitigation sequence could be applied to other audio biases such as those related to accent or age.
  • Standard evaluation protocols in audio spoofing benchmarks may need redesign to remove structural asymmetry across demographic groups.
  • Collecting more balanced training data alone is unlikely to resolve fairness issues if the root causes lie in feature representations.

Load-bearing premise

The identified sources of bias are the main causes and the pre-training diagnosis reliably indicates which mitigation strategies will succeed or fail.

What would settle it

An experiment in which per-gender threshold adjustment applied to the AASIST or Wav2Vec2+ResNet18 models on ASVSpoof5 fails to reduce measured gender unfairness while preserving accuracy would falsify the central mitigation claim.

Figures

Figures reproduced from arXiv: 2605.09087 by Aishwarya Fursule, Anderson R. Avila, Shruti Kshirsagar.

Figure 1
Figure 1. Figure 1: Source-to-mitigation pipeline mapping confirmed bias sources at [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Score distributions per gender on ASVSpoof5. Top row (Model 1 [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: t-SNE projections of 800 embeddings for Model 1 (left two columns) and Model 2 (right two columns), coloured by gender and label [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
read the original abstract

Audio deepfake detection systems are increasingly deployed in high-stakes security applications, yet their fairness across demographic groups remains critically underexamined. Prior work measures gender disparity but does not investigate where it comes from or how to fix it systematically. We present the first diagnosis-first framework that identifies bias source before applying targeted mitigation, evaluated on two models, AASIST and Wav2Vec2+ResNet18, on ASVSpoof5. Our diagnosis shows that bias does not stem from imbalanced training data but from acoustic representation differences, gender leakage in learned features, and structural evaluation asymmetry. We test mitigation strategies across in-processing, post-processing and combined families, including novel methods introduced in this work. Adjusting the decision threshold separately per gender reduces unfairness by 54% to 75% at no cost to detection accuracy, and our new epoch-level fairness regularisation method outperforms existing per-batch approaches. Adversarial debiasing succeeds only when gender leakage is localised, and fails when it is diffuse, an outcome correctly predicted by our diagnosis before training. No single method fully closes the fairness gap, confirming that bias sources must be identified before fixes are applied and that fairer benchmark design is equally important

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces a diagnosis-first framework for identifying sources of gender bias in audio deepfake detection and testing targeted mitigations. Evaluated on AASIST and Wav2Vec2+ResNet18 models using the ASVSpoof5 dataset, it concludes that observed gender disparities arise primarily from acoustic representation differences, gender leakage in learned features, and structural evaluation asymmetry, rather than from imbalanced training data. The work tests in-processing, post-processing, and combined mitigation strategies, including a novel epoch-level fairness regularization method, and reports that per-gender decision threshold adjustment reduces unfairness by 54-75% with no loss in detection accuracy. It further shows that adversarial debiasing succeeds only when leakage is localized, an outcome predicted by the pre-training diagnosis.

Significance. If the diagnoses and quantitative mitigation results hold under rigorous validation, this provides a practical systematic approach to fairness in high-stakes audio deepfake detection, emphasizing that bias sources must be identified before applying fixes and that fairer benchmark design is needed. Strengths include the explicit prediction of mitigation outcomes from diagnosis, the introduction of epoch-level regularization that outperforms per-batch baselines, and the finding that no single method fully closes the gap.

major comments (2)
  1. [Diagnosis of Bias Sources] The central claim that bias does not stem from imbalanced training data (abstract and diagnosis section) is based on observational checks of data distributions and feature statistics. To establish this attribution as primary, an interventional validation is required: retrain AASIST and Wav2Vec2+ResNet18 on a version of ASVSpoof5 with explicitly equalized male/female bonafide and spoof counts (via subsampling, oversampling, or reweighting), then re-measure EER/AUC gender gaps. Without this experiment, the diagnosis that acoustic differences, leakage, and asymmetry are the dominant sources remains unconfirmed, as unmeasured confounders could still contribute.
  2. [Mitigation Results] Table reporting the 54-75% unfairness reduction via per-gender thresholds (results section): the exact unfairness metric (e.g., EER gap or AUC disparity), confidence intervals or standard deviations across runs, and the precise definition of 'no cost to detection accuracy' (e.g., overall EER change) must be provided. The current quantitative claim is load-bearing for the post-processing mitigation recommendation but lacks these controls.
minor comments (2)
  1. [Abstract] The abstract states quantitative results (54-75% reduction) but omits the specific fairness metric, dataset splits, and number of runs; adding these would improve verifiability without altering the core contribution.
  2. [Proposed Methods] Notation for the epoch-level fairness regularization (method section) should include the explicit loss term and hyperparameter schedule to allow direct reproduction.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed review. We address each major comment below, indicating planned revisions to strengthen the manuscript while maintaining the integrity of our diagnosis-first approach.

read point-by-point responses

  1. Referee: [Diagnosis of Bias Sources] The central claim that bias does not stem from imbalanced training data (abstract and diagnosis section) is based on observational checks of data distributions and feature statistics. To establish this attribution as primary, an interventional validation is required: retrain AASIST and Wav2Vec2+ResNet18 on a version of ASVSpoof5 with explicitly equalized male/female bonafide and spoof counts (via subsampling, oversampling, or reweighting), then re-measure EER/AUC gender gaps. Without this experiment, the diagnosis that acoustic differences, leakage, and asymmetry are the dominant sources remains unconfirmed, as unmeasured confounders could still contribute.

    Authors: We appreciate the referee's call for stronger causal evidence. The diagnosis section supports our claim through observational analyses of training data distributions (showing near-balance in bonafide/spoof counts per gender), feature statistics, and leakage metrics that do not correlate with observed EER gaps. We agree an interventional experiment would further confirm the primary role of acoustic representation differences. In the revision, we will add results from retraining both models on a gender-equalized subset of ASVSpoof5 (via subsampling) and report the resulting EER/AUC gender gaps to directly test whether disparities persist. revision: yes

  2. Referee: [Mitigation Results] Table reporting the 54-75% unfairness reduction via per-gender thresholds (results section): the exact unfairness metric (e.g., EER gap or AUC disparity), confidence intervals or standard deviations across runs, and the precise definition of 'no cost to detection accuracy' (e.g., overall EER change) must be provided. The current quantitative claim is load-bearing for the post-processing mitigation recommendation but lacks these controls.

    Authors: We thank the referee for identifying this reporting gap. In the revised results section and associated table, we will explicitly define the unfairness metric as the absolute EER gap between genders. We will report standard deviations across five independent runs with different random seeds. We will also clarify that 'no cost to detection accuracy' means the overall EER changes by no more than 0.2% on average. These additions will provide the requested controls and transparency for the per-gender threshold adjustment results. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation; empirical diagnosis and mitigation tests remain independent of inputs.

full rationale

The paper's core chain—observational checks on training data balance, acoustic features, and leakage, followed by separate testing of threshold adjustment and epoch-level regularization—does not reduce any claimed prediction or source attribution to a fitted parameter or self-definition by construction. No equations equate outputs to inputs tautologically, no load-bearing self-citations justify uniqueness, and mitigations are evaluated on held-out metrics rather than renamed fits. The ruling-out of data imbalance rests on distributional statistics rather than interventional retraining, but this is an evidentiary gap, not circularity.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The framework rests on the assumption that bias sources can be reliably diagnosed from feature analysis and evaluation structure before mitigation; limited free parameters are implied in threshold tuning and regularization strength.

free parameters (2)
  • per-gender decision thresholds
    Adjusted separately to achieve fairness gains, with values chosen to balance accuracy and equity.
  • regularization strength for epoch-level fairness
    Hyperparameter controlling the new regularization method.
axioms (1)
  • domain assumption Gender bias in audio models can be attributed to acoustic representation differences, feature leakage, and evaluation asymmetry rather than data imbalance
    Central premise of the diagnosis step.

pith-pipeline@v0.9.0 · 5529 in / 1246 out tokens · 47286 ms · 2026-05-12T02:42:17.859008+00:00 · methodology

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Lean theorems connected to this paper

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Reference graph

Works this paper leans on

33 extracted references · 33 canonical work pages · 1 internal anchor

  1. [1]

    Audio deepfake detection using deep learning,

    O. A. Shaaban and R. Yildirim, “Audio deepfake detection using deep learning,”Engineering Reports, vol. 7, no. 3, p. e70087, 2025

    work page 2025

  2. [2]

    A Comprehensive Survey of Deep- Fake Generation and Detection Techniques in Audio-Visual Media,

    I. Khan, K. Khan, and A. Ahmad, “A Comprehensive Survey of Deep- Fake Generation and Detection Techniques in Audio-Visual Media,” ICCK Journal of Image Analysis and Processing, vol. 1, no. 2, pp. 73- 95, 2025

    work page 2025

  3. [3]

    ASVspoof 5: Crowdsourced speech data, deepfakes, and adversarial attacks at scale,

    X. Wang et al., “ASVspoof 5: Crowdsourced speech data, deepfakes, and adversarial attacks at scale,”arXiv:2408.08739, 2024

    work page arXiv 2024

  4. [4]

    End-to-end anti-spoofing with RawNet2,

    H. Tak et al., “End-to-end anti-spoofing with RawNet2,” inProc. ICASSP, 2021, pp. 6369-6373

    work page 2021

  5. [5]

    AASIST: Audio anti-spoofing using integrated spectro-temporal graph attention networks,

    J. W. Jung et al., “AASIST: Audio anti-spoofing using integrated spectro-temporal graph attention networks,” inProc. ICASSP, 2022, pp. 6367-6371

    work page 2022

  6. [6]

    WavLM: Large-scale self-supervised pre-training for full stack speech processing,

    S. Chen et al., “WavLM: Large-scale self-supervised pre-training for full stack speech processing,”IEEE J. Sel. Topics Signal Process., vol. 16, no. 6, pp. 1505-1518, 2022

    work page 2022

  7. [7]

    wav2vec 2.0: A framework for self-supervised learning of speech representations,

    A. Baevski, Y . Zhou, A. Mohamed, and M. Auli, “wav2vec 2.0: A framework for self-supervised learning of speech representations,” NeurIPS, vol. 33, pp. 12449-12460, 2020

    work page 2020

  8. [8]

    Sonar: A synthetic AI-audio detection framework and benchmark,

    X. Li, P.-Y . Chen, and W. Wei, “Sonar: A synthetic AI-audio detection framework and benchmark,” 2024

    work page 2024

  9. [9]

    Acoustic analysis of speech,

    R. D. Kent and Y . Kim, “Acoustic analysis of speech,” inThe Handbook of Clinical Linguistics, Wiley-Blackwell, 2008, pp. 360- 380

    work page 2008

  10. [10]

    Bias in data-driven artificial intelligence systems,

    E. Ntoutsi et al., “Bias in data-driven artificial intelligence systems,” WIREs Data Mining Knowl. Discov., vol. 10, no. 3, p. e1356, 2020

    work page 2020

  11. [11]

    Fair voice biometrics: Impact of demographic imbal- ance on group fairness in speaker recognition,

    G. Fenu et al., “Fair voice biometrics: Impact of demographic imbal- ance on group fairness in speaker recognition,” inProc. Interspeech, 2021, pp. 1892-1896

    work page 2021

  12. [12]

    Real-time detection of AI-generated speech for deepfake voice conversion,

    J. J. Bird and A. Lotfi, “Real-time detection of AI-generated speech for deepfake voice conversion,”arXiv:2308.12734, 2023

    work page arXiv 2023

  13. [13]

    FairSSD: Understanding bias in synthetic speech detectors,

    A. K. S. Yadav et al., “FairSSD: Understanding bias in synthetic speech detectors,” inProc. CVPR, 2024, pp. 4418-4428

    work page 2024

  14. [14]

    Evaluation of the Human Capacity to Detect Spanish Deepfake Audios with a Paraguayan Accent,

    M. V . Gim ´enez Ramos et al., “Evaluation of the Human Capacity to Detect Spanish Deepfake Audios with a Paraguayan Accent,”Applied Sciences, vol. 16, no. 4, p. 1910, 2026

    work page 1910

  15. [15]

    Gender Fairness in Audio Deepfake Detection: Performance and Disparity Analysis

    A. Fursule, S. Kshirsagar, and A. R. Avila, “Gender Fairness in Audio Deepfake Detection: Performance and Disparity Analysis,” arXiv:2603.09007, 2026

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  16. [16]

    Fairness without demographics in repeated loss minimization,

    T. Hashimoto et al., “Fairness without demographics in repeated loss minimization,” inProc. ICML, 2018, pp. 1929-1838

    work page 2018

  17. [17]

    Bias Mitigation Strategies in AI,

    J. Foley, “Bias Mitigation Strategies in AI,”TechRxiv, Jan. 2026

    work page 2026

  18. [18]

    AFSS: Artifact-Focused Self-Synthesis for Mitigating Bias in Audio Deepfake Detection,

    H.-S. Nguyen-Le et al., “AFSS: Artifact-Focused Self-Synthesis for Mitigating Bias in Audio Deepfake Detection,”arXiv:2603.26856, 2026

    work page arXiv 2026

  19. [19]

    A survey on bias and fairness in machine learning,

    N. Mehrabi et al., “A survey on bias and fairness in machine learning,” ACM Comput. Surv., vol. 54, no. 6, pp. 1-35, 2021

    work page 2021

  20. [20]

    A review on fairness in machine learning,

    D. Pessach and E. Shmueli, “A review on fairness in machine learning,”ACM Comput. Surv., vol. 55, no. 3, pp. 1-44, 2022

    work page 2022

  21. [21]

    GBDF: Gender balanced deepfake dataset towards fair deepfake detection,

    A. V . Nadimpalli and A. Rattani, “GBDF: Gender balanced deepfake dataset towards fair deepfake detection,” inProc. ICPR, 2022, pp. 320- 337

    work page 2022

  22. [22]

    Improving fairness in deepfake detection,

    Y . Ju et al., “Improving fairness in deepfake detection,” inProc. WACV, 2024, pp. 4655-4665

    work page 2024

  23. [23]

    Preserving fairness generalization in deepfake detection,

    L. Lin et al., “Preserving fairness generalization in deepfake detection,” inProc. CVPR, 2024, pp. 16815-16825

    work page 2024

  24. [24]

    Unsupervised domain adaptation by backpropagation,

    Y . Ganin and V . Lempitsky, “Unsupervised domain adaptation by backpropagation,” inProc. ICML, 2015, pp. 1180-1189

    work page 2015

  25. [25]

    Fairness Through Awareness,

    C. Dwork et al., “Fairness Through Awareness,” inProc. ITCS, ACM, 2012, pp. 214-226

    work page 2012

  26. [26]

    Equality of Opportunity in Supervised Learning,

    M. Hardt, E. Price, and N. Srebro, “Equality of Opportunity in Supervised Learning,” inNeurIPS, vol. 29, 2016

    work page 2016

  27. [27]

    Fair Prediction with Disparate Impact,

    A. Chouldechova, “Fair Prediction with Disparate Impact,”Big Data, vol. 5, no. 2, pp. 153-163, 2017

    work page 2017

  28. [28]

    Fairness in Criminal Justice Risk Assessments,

    R. Berk et al., “Fairness in Criminal Justice Risk Assessments,” Sociological Methods & Research, vol. 50, no. 1, pp. 3-44, 2021

    work page 2021

  29. [29]

    Phonetic Analysis of Real and Synthetic Speech Using HuBERT Embeddings: Perspectives for Deepfake Detection,

    D. E. Temmar, A. Hamadene, V . Nallaguntla, A. Fursule, M. S. Allili et al., “Phonetic Analysis of Real and Synthetic Speech Using HuBERT Embeddings: Perspectives for Deepfake Detection,” inProc. IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2025, pp. 86-91

    work page 2025

  30. [30]

    PhonemeDF: A Synthetic Speech Dataset for Audio Deepfake De- tection and Naturalness Evaluation,

    V . Nallaguntla, A. Fursule, S. Kshirsagar, and A. R. Avila, “PhonemeDF: A Synthetic Speech Dataset for Audio Deepfake De- tection and Naturalness Evaluation,”arXiv:2603.15037, 2026

    work page arXiv 2026

  31. [31]

    Investigating the Impact of Speech Enhancement on Audio Deepfake Detection in Noisy Environments,

    S. Kshirsagar and A. R. Avila, “Investigating the Impact of Speech Enhancement on Audio Deepfake Detection in Noisy Environments,” arXiv preprint, 2025

    work page 2025

  32. [32]

    Ge- ographic Bias Analysis and Cross-Domain Generalization in Deep Learning-Based Building Damage Assessment,

    S. Kshirsagar, B. Chandra, U. Tallal, R. Bagai, and A. Dutta, “Ge- ographic Bias Analysis and Cross-Domain Generalization in Deep Learning-Based Building Damage Assessment,”arXiv preprint, 2025

    work page 2025

  33. [33]

    The Impact of Ideological Discourses in RAG: A Case Study with COVID-19 Treatments,

    E. Salari, M. C. N. Delfino, H. Amamou, J. V . de Souza, S. Kshirsagar, A. Davoust et al., “The Impact of Ideological Discourses in RAG: A Case Study with COVID-19 Treatments,”arXiv:2603.14838, 2026

    work page arXiv 2026