cs.SDMar 9, 2026

Gender Fairness in Audio Deepfake Detection: Performance and Disparity Analysis

Authors: Aishwarya Fursule, Shruti Kshirsagar, Anderson R. Avila

Organizations: School of Computing, Wichita State University, Wichita, KS, USA · Institut national de la recherche scientifique (INRS–EMT), Montreal, QC, Canada · INRS-UQO Mixed Research Unit on Cybersecurity, Gatineau, Canada

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Abstract

Audio deepfake detection aims to detect real human voices from those generated by Artificial Intelligence (AI) and has emerged as a significant problem in the field of voice biometrics systems. With the ever-improving quality of synthetic voice, the probability of such a voice being exploited for illicit practices like identity thest and impersonation increases. Although significant progress has been made in the field of Audio Deepfake Detection in recent times, the issue of gender bias remains underexplored and in its nascent stage In this paper, we have attempted a thorough analysis of gender dependent performance and fairness in audio deepfake detection models. We have used the ASVspoof 5 dataset and train a ResNet-18 classifier and evaluate detection performance across four different audio features, and compared the performance with baseline AASIST model. Beyond conventional metrics such as Equal Error Rate (EER %), we incorporated five established fairness metrics to quantify gender disparities in the model. Our results show that even when the overall EER difference between genders appears low, fairness-aware evaluation reveals disparities in error distribution that are obscured by aggregate performance measures. These findings demonstrate that reliance on standard metrics is unreliable, whereas fairness metrics provide critical insights into demographic-specific failure modes. This work highlights the importance of fairness-aware evaluation for developing a more equitable, robust, and trustworthy audio deepfake detection system.

Figures & tables

Fig. 1: The five group fairness metrics adopted in this study.
Fig. 2: Balanced gender distribution for the ASVspoof 5 dataset across train, development, and evaluation splits.
Fig. 3: Illustration of the procedure adopted to perform group fairness assessment. Model performance is based on 5 group fairness measures.
ModelFemaleMaleDiff (F-M)p-value (Holm)
AASIST0.1900.210-0.016<1×10−16<1\times 10^{-16}
CQT0.3800.2900.090<1×10−16<1\times 10^{-16}
LogSpec0.3240.334-0.009<1×10−16<1\times 10^{-16}
Wav2vec0.3490.3160.033<1×10−16<1\times 10^{-16}
WavLM0.1950.1820.011<1×10−16<1\times 10^{-16}
TABLE I: Fairness assessment based on the statistical parity metric.
ModelFemaleMaleDiff (F-M)p-value (Holm)
AASIST0.1720.197-0.007<1×10−16<1\times 10^{-16}
CQT0.4740.3600.114<1×10−16<1\times 10^{-16}
LogSpec0.4980.501-0.0030.2171
Wav2vec0.6600.6310.029<1×10−16<1\times 10^{-16}
WavLM0.5430.5110.032<1×10−16<1\times 10^{-16}
TABLE II: Fairness assessment based on the equal opportunity metric.
ModelFemaleMaleDiff (F-M)p-value (Holm)
AASIST0.2790.358-0.078<1×10−16<1\times 10^{-16}
CQT0.3570.2710.086<1×10−16<1\times 10^{-16}
LogSpec0.2820.289-0.007<1×10−16<1\times 10^{-16}
Wav2vec0.2730.2310.042<1×10−16<1\times 10^{-16}
WavLM0.1100.0960.014<1×10−16<1\times 10^{-16}
TABLE III: Fairness assessment based on the equality of odds metric.
ModelFemaleMaleDiff (F-M)p-value (Holm)
AASIST0.6790.687-0.0070.003
CQT0.2440.263-0.019<1×10−16<1\times 10^{-16}
LogSpec0.3010.318-0.017<1×10−16<1\times 10^{-16}
Wav2vec0.3710.424-0.053<1×10−16<1\times 10^{-16}
WavLM0.5450.5891-0.044<1×10−16<1\times 10^{-16}
TABLE IV: Fairness assessment based on the predictive parity metric.
ModelFemaleMaleDiff (F-M)p-value (Holm)
AASIST0.09770.0991-0.0014<1×10−16<1\times 10^{-16}
CQT2.79041.5741.216<1×10−16<1\times 10^{-16}
LogSpec0.7340.749-0.015<1.32×10−16<1.32\times 10^{-16}
Wav2vec3.3112.3310.979<1×10−16<1\times 10^{-16}
WavLM0.9930.7290.263<1×10−16<1\times 10^{-16}
TABLE V: Fairness assessment based on the treatment equality metric.

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