The paper introduces Traceable TTS, a framework that enables Text‑to‑Speech systems to attribute synthesized speech to their source models without embedding explicit watermarks. By jointly training the TTS model and a discriminator, the method improves traceability generalization while maintaining or slightly enhancing audio quality. This represents the first attempt at watermark‑free TTS with strong traceability, and the authors plan to release the code to support further research.
arXiv:2610.15082v1 Announce Type: cross Abstract: As text-to-speech (TTS) systems continue to advance, provenance tracking and security verification of synthesized speech have become increasingly imp...
arXiv:2606. 11828v1 Announce Type: cross Abstract: Audio watermarking aims to embed identifiable information into audio while remaining imperceptible.
Audio watermarking aims to embed identifiable information into audio while remaining imperceptible. Existing methods adopt high-fidelity, low-energy designs to preserve perceptual quality, but the resulting watermarks lack robustness under suppression by speech reconstruction models.
Provenance watermarking is increasingly treated as a safeguard for synthetic speech, whether built directly into speech-generation models such as Chatterbox, provided through dedicated techniques such as AudioSeal, or deployed by commercial platforms such as ElevenLabs. We identify a previously uncharacterized liability: when synthetic speech is watermarked and human speech is not, detectors trained alongside latch onto the watermark as a spurious "watermark => fake" shortcut.
arXiv:2606. 23335v2 Announce Type: replace-cross Abstract: Provenance watermarking is increasingly treated as a safeguard for synthetic speech, whether built directly into speech-generation models such as Chatterbox, provided through dedicated techniques such as AudioSeal, or deployed by commercial platforms such as ElevenLabs.
The paper introduces CRAW, a codec‑robust audio watermarking framework designed to embed imperceptible signals into synthetic speech. CRAW enhances robustness against neural re‑synthesis, codecs, denoisers, and vocoders while preserving high perceptual quality through distortion‑aware training, attention‑based pooling, perceptual masking, and error‑correcting codes. Experiments show CRAW outperforms existing post‑hoc watermarking methods in robustness without compromising audio quality.
CRAW is a codec‑robust audio watermarking framework designed to embed imperceptible signals into synthetic speech, enabling provenance verification. It improves robustness against neural re‑synthesis, codecs, denoisers, and vocoders while preserving perceptual quality through distortion‑aware training, attention‑based pooling, perceptual masking, and error‑correcting codes. Experiments show CRAW outperforms existing post‑hoc watermarking methods in robustness without compromising audio quality.
AI music generation has rapidly advanced alongside commercial platforms, raising the need for reliable watermarking for provenance and attribution. However, existing audio watermarking research has largely focused on speech, and applying speech-oriented methods to music is challenging due to music's complex structure and rich acoustic texture.
The paper introduces a training‑free proactive defense for detecting partial deepfake speech by using self‑embedding steganography. It embeds a compressed version of the clean audio within itself, allowing post‑hoc extraction of reference content and enabling detection of spoofed segments via codec‑based restoration. Experiments on a benchmark dataset show that this method complements passive detectors and operates without any training, offering a robust, data‑efficient alternative for partial deepfake detection.
arXiv:2607. 11117v1 Announce Type: cross Abstract: AI music generation has rapidly advanced alongside commercial platforms, raising the need for reliable watermarking for provenance and attribution.
arXiv:2508.03829v3 Announce Type: replace Abstract: The growing deployment of Large Language Models (LLMs) has raised concerns about their misuse in generating harmful or deceptive content. To addres...
arXiv:2607. 16870v1 Announce Type: cross Abstract: End-to-end speech language models increasingly represent user speech with speech tokens rather than relying exclusively on cascaded ASR--LLM--TTS pipelines.