arXiv:2606.05101v1 Announce Type: cross 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 fa
The proliferation of sophisticated text-to-speech (TTS) models necessitates advanced methods for detecting deepfakes, pushing the development of red-teaming tools.
This development allows for robust evaluation and strengthening of audio deepfake detection (ADD) models, crucial for countering malicious use of generative AI and maintaining trust in digital audio.
The introduction of FoeGlass changes the landscape of ADD model evaluation by providing an automated, black-box red-teaming capability, moving beyond manual and inefficient dataset creation.
- · AI safety researchers
- · Cybersecurity firms
- · Audio deepfake detection developers
- · Platforms relying on audio authenticity
- · Malicious actors using deepfakes
- · Inefficient deepfake detection methods
Improved resilience and accuracy of audio deepfake detection models.
Increased difficulty for attackers to deploy undetectable audio deepfakes, potentially leading to more sophisticated adversarial attacks.
Enhanced public trust in audio content, or conversely, a continuous arms race between deepfake generation and detection technologies.
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