arXiv:2606.29544v1 Announce Type: cross Abstract: We present Proteus, a framework developed at Resemble AI for automated robustness testing of our audio deepfake detection system. Given a detector, Proteus systematically searches over sequences of everyday audio transformations (codec transcoding, additive noise, reverberation, dynamic-range compression, and VoIP simulation) to find combinations that fool the detector while preserving speech quality. We propose two complementary search strategies: (1) a breadth-first search that exhaustively maps augmentation effectiveness across the parameter
The rapid proliferation of deepfake technology necessitates robust detection methods, driving research into automated testing to keep pace with evolving threats.
Sophisticated deepfake detection is critical for maintaining trust in digital media, securing financial transactions, and preventing misinformation, directly impacting national security and economic stability.
The development of automated adversarial testing frameworks for deepfake detectors improves the resilience and reliability of these systems, making them harder to circumvent.
- · AI security firms
- · Deepfake detection developers
- · Digital media platforms
- · Voice authentication services
- · Deepfake creators
- · Misinformation agents
Automated testing will accelerate the development of more robust audio deepfake detection systems.
Improved detection capabilities will make it harder for malicious actors to successfully deploy audio deepfakes, potentially shifting their focus to other attack vectors.
The arms race between deepfake generation and detection could lead to the development of 'un-deepfakable' media or, conversely, highly adaptable, undetectable deepfakes.
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Read at arXiv cs.AI