arXiv:2606.10911v1 Announce Type: cross Abstract: Claims about the robustness and fairness of deepfake speech detectors are only as credible as the datasets used to train and evaluate those systems. We present a dataset-level audit of the deepfake speech landscape. We compile and analyze 39 deepfake speech datasets, examining key attributes including accessibility, documentation, demographic and language coverage, dataset scale, and the underlying bona fide speech sources. Our audit reveals two important takeaways. Firstly, fairness assessment is largely infeasible because most datasets lack d
The proliferation of deepfake technology necessitates robust detection methods, making the quality of training datasets a critical and immediate concern.
The integrity and fairness of AI systems designed to combat deepfakes depend entirely on the representativeness and ethical construction of their underlying datasets, affecting trust in digital media and AI itself.
Understanding the widespread deficiencies in deepfake speech datasets highlights a major bottleneck in developing equitable and effective deepfake detection, shifting focus to dataset quality over model architecture alone.
- · Ethical AI research organizations
- · Data auditors and curators
- · Developers of robust, unbiased datasets
- · Developers relying on flawed deepfake datasets
- · Less rigorous AI research
- · Public confidence in deepfake detection
Increased focus and investment in creating high-quality, ethically sourced deepfake speech datasets.
Improved fairness and robustness in next-generation deepfake detection models.
Enhanced ability to combat misinformation and manipulation through audio deepfakes, strengthening digital security and public trust.
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