MixFake: Benchmarking and Enhancing Audio Deepfake Detection in Diverse Real-world Mixed Audio
Abstract
Speech deepfake detection has achieved remarkable success in clean environments but faces significant challenges in complex, real-world scenarios where speech is often mixed with background music or noise. Current state-of-the-art methods rely on semantic features from self-supervised learning (SSL) models, which often fail when processing non-speech or mixed-source audio. In this paper, we first introduce MixFake, a large-scale benchmark dataset designed to simulate diverse acoustic environments with varying SNR levels and mixed authenticity components. To address the "semantic-centric" limitation, we propose a Multi-stream Prompt Tuning framework that injects signal-level priors into SSL backbones. By integrating base, frequency, and texture streams through deep prompt injection, our model effectively captures acoustic artifacts. Experimental results demonstrate that our method significantly outperforms existing baselines, achieving a 0.95% EER in foreground detection and a substantial 7.72% absolute improvement in complex background detection tasks. Our dataset and code are available at https://github.com/saltfish233/MixFake.
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
This paper presents a significant advancement in audio deepfake detection by introducing a novel dataset and a multi-stream framework that enhances detection capabilities in complex acoustic environments. The comprehensive methodology and rigorous experimental evaluation contribute to its potential impact on the field of machine learning and audio processing.
Methodology
The proposed methodology introduces a novel Multi-stream Prompt Tuning framework that enhances the capabilities of self-supervised learning models by integrating signal-level acoustic priors through three distinct streams (Base, Frequency, and Texture). This approach effectively addresses the limitations of existing models in detecting audio deepfakes in complex mixed environments, making it a significant advancement in the field. The use of the Hilbert-Huang Transform and Teager-Kaiser Energy Operator for feature extraction is particularly innovative, allowing for a more nuanced understanding of audio signals. The systematic construction of the MixFake dataset, which simulates real-world acoustic environments, further strengthens the methodological framework by providing a robust testing ground for the proposed techniques.
Results & evaluation
The experimental evaluation is comprehensive, utilizing a large-scale dataset (MixFake) that includes a variety of audio scenarios, including both single-source and mixed-source conditions. The results demonstrate a significant improvement over existing state-of-the-art models, particularly in challenging background detection tasks. The use of Equal Error Rate (EER) as a performance metric is appropriate for the domain, and the reported improvements in EER values substantiate the effectiveness of the proposed method. The ablation studies provide additional insights into the contributions of each component of the framework, reinforcing the validity of the results.
Reproducibility
The paper provides sufficient implementation details, including the architecture of the model, training strategies, and evaluation metrics, which facilitates reproducibility. The availability of the dataset and code on GitHub is a strong point, as it allows other researchers to validate the findings and build upon the work. However, the paper could benefit from more detailed hyperparameter settings and training conditions to enhance reproducibility further.
Limitations
One limitation of the study is the potential overfitting to the specific conditions of the MixFake dataset, as the performance may not generalize to all real-world scenarios. Additionally, while the dataset covers a range of SNR levels, it may not encompass all possible acoustic environments encountered in practice. The reliance on SSL models may also limit the approach's applicability in scenarios where labeled data is scarce or unavailable.
Broader impact
The implications of this research are significant, as it addresses a pressing issue in the realm of audio deepfake detection, which has far-reaching consequences in security, privacy, and misinformation. The development of robust detection methods can help mitigate the risks associated with malicious use of speech synthesis technologies. Furthermore, the MixFake dataset can serve as a valuable resource for future research in audio processing and deepfake detection, fostering advancements in the field. This paper presents a significant advancement in audio deepfake detection by introducing a novel dataset and a multi-stream framework that enhances detection capabilities in complex acoustic environments. The comprehensive methodology and rigorous experimental evaluation contribute to its potential impact on the field of machine learning and audio processing.
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