ScrambleMark: Zero-Query Watermark-Agnostic Black-Box Evasion of Modern Speech Watermarking

Researchers demonstrate a zero-query black-box attack called ScrambleMark that can evade modern speech watermarking systems without requiring access to the watermarking model or training data. The attack uses adversarial audio perturbations to remove watermarks while preserving speech quality, raising concerns about the robustness of watermarking as a defense mechanism for protecting AI-generated speech.

Researchers demonstrate a zero-query black-box attack called ScrambleMark that can evade modern speech watermarking systems without requiring access to the watermarking model or training data. The attack uses adversarial audio perturbations to remove watermarks while preserving speech quality, raising concerns about the robustness of watermarking as a defense mechanism for protecting AI-generated speech. This finding has significant implications for enterprises using speech watermarking to detect synthetic audio in regulated industries.

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