FoeGlass: Simple In-Context Learning Is Enough for Red Teaming Audio Deepfake Detectors
Abstract
Audio deepfake detection (ADD) models are critical for countering the malicious use of text-to-speech (TTS) models. Evaluating and strengthening ADD models requires developing datasets that span the space of generated audio and highlight high-error regions. Existing dataset development strategies face two challenges: (i) manual collection, and (ii) inefficient discovery of blind spots in the ADD models. To address these challenges, we propose FoeGlass, the first black-box automated red-teaming method for ADDs, which effectively discovers ADD failure modes in the space of generated audio underexplored by state-of-the-art deepfake benchmarks. FoeGlass uses the in-context learning capabilities of an LLM to explore the input space of a TTS model, generating audio samples that fool the target ADD using only black-box access to all components. By using a carefully designed context based on diversity measurements, FoeGlass mitigates the common problem of mode collapse in automated red-teaming systems. Empirical evaluations on several open-source ADD and TTS models demonstrate that data generated from FoeGlass substantially improves the false negative rates over unconditional sampling baselines and recent spoofing datasets by up to 94%, while requiring no manual supervision. Furthermore, we show that the attacks generated by FoeGlass are transferable across different target ADDs, demonstrating its broad applicability and ease of use for the automated red teaming of ADD systems. Finally, fine-tuning ADD models on FoeGlass-generated samples notably enhances the robustness of the detectors (up 41%).
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 main contribution of this paper is the introduction of FoeGlass, an automated red-teaming method for audio deepfake detectors that utilizes in-context learning from LLMs to efficiently generate diverse and effective adversarial samples. This work represents a significant advancement in the evaluation and strengthening of ADDs, addressing critical gaps in existing methodologies and datasets.
Methodology
The proposed methodology, FoeGlass, is innovative in its use of in-context learning from large language models (LLMs) to automate the red-teaming process for audio deepfake detectors (ADDs). By leveraging a black-box approach, it circumvents the need for manual input generation and enables the discovery of failure modes in ADDs that are often overlooked by existing datasets. The incorporation of diversity feedback to mitigate mode collapse is a significant enhancement that contributes to the robustness of the generated attacks. The design of the context for the LLM, which includes both successful and failed attempts, is well thought out and effectively guides the model towards generating more effective adversarial examples.
Results & evaluation
The empirical evaluations conducted on various open-source ADD and TTS models demonstrate the effectiveness of FoeGlass in significantly reducing false negative rates (FNRs) compared to unconditional sampling baselines. The reported improvements of up to 94% in FNR highlight the method's potential to enhance the robustness of ADDs. The experiments are comprehensive, testing multiple models and configurations, which strengthens the validity of the results. However, the paper could benefit from more extensive comparisons with other state-of-the-art methods to further contextualize its performance.
Reproducibility
The paper provides sufficient implementation details, including the models used and the experimental setup. However, the absence of publicly available code or data limits reproducibility. Providing a GitHub repository or similar resource would enhance the ability of other researchers to replicate the findings and build upon this work.
Limitations
One limitation noted is the reliance on hyperparameter tuning, particularly for the diversity threshold and context length, which can affect the success and diversity of generated attacks. Additionally, the method's effectiveness on commercial ADDs remains untested, which may limit its applicability in real-world scenarios. The potential for misuse of the technology is also a concern, as highlighted in the impact statement.
Broader impact
The development of automated red-teaming methods like FoeGlass has significant implications for the security and robustness of audio deepfake detection systems, which are increasingly critical in combating misinformation and fraud. By improving the evaluation of ADDs, this work contributes to the broader field of machine learning security. However, the potential for malicious use of such technologies necessitates careful consideration and the development of corresponding defense mechanisms. The main contribution of this paper is the introduction of FoeGlass, an automated red-teaming method for audio deepfake detectors that utilizes in-context learning from LLMs to efficiently generate diverse and effective adversarial samples. This work represents a significant advancement in the evaluation and strengthening of ADDs, addressing critical gaps in existing methodologies and datasets.
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