Probing Speaker Identity Sensitivity in Audio Deepfake Detectors
Abstract
Audio deepfake detectors are trained to distinguish genuine speech from synthetic speech and often perform well on standard benchmarks. Yet the same detector that achieves less than 1% error on one dataset can see its error rate increase twentyfold when evaluated on a different dataset. We argue that one contributing factor is speaker-identity reliance: standard training corpora correlate speaker identity with the genuine/synthetic label, allowing detectors to partially rely on speaker-related cues rather than synthesis artifacts alone. We propose the Identity Sensitivity Score (ISS), a per-utterance diagnostic that quantifies how much a detector's output changes across different speaker identity contexts. ISS requires no ground-truth labels at inference time and can be computed from the detector score and a pool of reference speaker examples. Across two detectors and two datasets, incorrectly classified utterances have ISS scores 29 to 52 times higher than correctly classified utterances, and ISS alone predicts misclassification with area-under-curve (AUC) up to 0.954. To test whether ISS actually captures identity-sensitive behavior rather than serving only as a proxy for prediction confidence, we apply voice conversion to 500 utterances and measure the resulting detector-score shift. Utterances flagged as identity-sensitive by ISS respond 19 to 30 times more strongly to this manipulation than utterances flagged as stable. These results position ISS as a practical inference-time diagnostic for speaker-dependent failure analysis in audio deepfake detection.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper introduces the Identity Sensitivity Score (ISS), a label-free diagnostic metric that quantifies speaker-identity reliance in audio deepfake detectors, demonstrating that identity-sensitive predictions are strongly correlated with misclassification and distributional shift failures.
Methodology
The paper proposes the Identity Sensitivity Score (ISS), a novel diagnostic metric designed to quantify the extent to which an audio deepfake detector relies on speaker identity cues rather than synthesis-specific artifacts. The methodology is theoretically grounded in the sensitivity of sigmoid decision boundaries; it injects speaker identity context via logit-space perturbation using external ECAPA-TDNN embeddings. This approach is clever because it is label-free at inference time, requiring only the detector's output and a pool of reference speaker embeddings. The method avoids the computational cost of generating new audio samples for probing, instead manipulating the internal representation space. The theoretical motivation linking ISS to decision-boundary proximity provides a strong mathematical justification for why high-ISS scores correlate with misclassification, distinguishing it from standard uncertainty measures like entropy or margin which are monotonic functions of the prediction score itself.
Results & evaluation
The experimental evaluation is rigorous and well-structured. The authors evaluate ISS across two architecturally distinct detectors (AASIST and RawNet2) and two datasets (ASVspoof 2019 LA and 2021 LA), demonstrating the metric's robustness across different model families. The core finding—that incorrectly classified utterances have ISS scores 29 to 52 times higher than correctly classified ones—is compelling. The use of Voice Conversion (FreeVC) as a controlled intervention to validate that ISS captures genuine identity sensitivity rather than just boundary proximity is a strong empirical contribution. The statistical significance is well-established with Mann-Whitney U tests and Cohen's d. The analysis of attack types (TTS vs. VC vs. Hybrid) adds depth, revealing that different failure modes (identity-driven vs. artifact-driven) can be distinguished by ISS, a nuance lost in aggregate metrics.
Reproducibility
The paper provides sufficient detail for reproduction. The datasets (ASVspoof 2019/2021 LA) are standard benchmarks. The detectors (AASIST, RawNet2) are widely available. The external speaker model (ECAPA-TDNN) is also standard. The specific configuration parameters ($K=10$, $\lambda=5.0$) are provided. However, the paper notes that $\lambda$ tuning requires a small labeled development set, which is a minor practical hurdle but does not prevent reproduction. The code is not explicitly linked in the provided text, but the methodology is clear enough that implementation is straightforward.
Limitations
The authors correctly identify several limitations. ISS relies on an external speaker embedding model, meaning it measures sensitivity to identity as represented in ECAPA-TDNN's space, which may not perfectly align with the deepfake detector's internal identity representations. The method requires a small labeled development set for hyperparameter tuning ($\lambda$), although this is a one-time offline step. The evaluation is limited to two detectors and two datasets; extending to SSL-based detectors (e.g., Wav2Vec2) is noted as future work. Furthermore, while ISS diagnoses the *cause* of potential failure (identity reliance), it does not inherently correct it; training interventions to mitigate this reliance are outside the scope of this work.
Broader impact
This work has significant implications for the reliability and trustworthiness of audio deepfake detection systems, which are critical for security, forensics, and media integrity. By providing a tool to diagnose *why* a detector fails (identity bias vs. artifact ambiguity), ISS enables more targeted improvements in model training and deployment. It highlights a systemic issue in current benchmarks where speaker identity correlates with labels, potentially leading to over-optimistic performance estimates. This diagnostic capability can help researchers build more robust, generalizable detectors that rely on genuine synthesis artifacts rather than spurious correlations. The paper introduces the Identity Sensitivity Score (ISS), a label-free diagnostic metric that quantifies speaker-identity reliance in audio deepfake detectors, demonstrating that identity-sensitive predictions are strongly correlated with misclassification and distributional shift failures.
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