LambdaMark is the first generic radioactive audio watermark that injects multi-bit messages into semantic latent representations, achieving robustness to distortions and removal attacks even after downstream model finetuning.
Wavmark: Watermarking for audio generation
11 Pith papers cite this work. Polarity classification is still indexing.
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MelShield adds keyed low-energy spread-spectrum perturbations to Mel-spectrograms inside TTS pipelines before vocoding to enable robust extraction of user-specific attribution signals even after compression or noise.
LAVA is a layered audio-visual watermarking system using cross-modal fusion and calibration-aware alignment to achieve robust deepfake tamper detection and localization under compression and asynchrony.
DiffErase removes black-box audio watermarks via diffusion priors by adding intermediate noise and regenerating with a pretrained model, preserving quality across audio domains.
A training-free audio watermarking method that reduces vocabulary via community detection to boost detection robustness by orders of magnitude while resisting audio modifications.
APC embeds compact Ed25519 signatures into audio phase data with error correction to achieve 97.5-98.3% cryptographic verification under eight attack types at mean PESQ 3.02.
StreamMark trains an Encoder-Distortion-Decoder network to embed semi-fragile watermarks that remain recoverable after benign audio transformations but drop to random accuracy under voice conversion and editing attacks.
A black-box audio watermark removal attack trained on limited samples that generalizes across datasets and watermark schemes with high attack success rates.
Feature-aligned watermarking embeds a codec-generated pseudo-speech signal into the spectrogram to raise robustness against reconstruction models while keeping imperceptibility comparable to prior methods.
XAttnMark is a new neural audio watermarking method using partial parameter sharing, cross-attention for message retrieval, temporal conditioning, and a psychoacoustic TF masking loss that reports state-of-the-art detection and attribution robustness.
citing papers explorer
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LambdaMark: Semantic Audio Watermarking for Robustness and Radioactivity
LambdaMark is the first generic radioactive audio watermark that injects multi-bit messages into semantic latent representations, achieving robustness to distortions and removal attacks even after downstream model finetuning.
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MelShield: Robust Mel-Domain Audio Watermarking for Provenance Attribution of AI Generated Synthesized Speech
MelShield adds keyed low-energy spread-spectrum perturbations to Mel-spectrograms inside TTS pipelines before vocoding to enable robust extraction of user-specific attribution signals even after compression or noise.
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LAVA: Layered Audio-Visual Anti-tampering Watermarking for Robust Deepfake Detection and Localization
LAVA is a layered audio-visual watermarking system using cross-modal fusion and calibration-aware alignment to achieve robust deepfake tamper detection and localization under compression and asynchrony.
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Audio Pirates: Black-box Audio Watermark Removal via Diffusion Priors
DiffErase removes black-box audio watermarks via diffusion priors by adding intermediate noise and regenerating with a pretrained model, preserving quality across audio domains.
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Hidden in Plain Tokens: Simply Robust, Gradient-Free Watermark for Synthetic Audio
A training-free audio watermarking method that reduces vocabulary via community detection to boost detection robustness by orders of magnitude while resisting audio modifications.
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Asymmetric Phase Coding Audio Watermarking
APC embeds compact Ed25519 signatures into audio phase data with error correction to achieve 97.5-98.3% cryptographic verification under eight attack types at mean PESQ 3.02.
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StreamMark: A Deep Learning-Based Semi-Fragile Audio Watermarking for Proactive Deepfake Detection
StreamMark trains an Encoder-Distortion-Decoder network to embed semi-fragile watermarks that remain recoverable after benign audio transformations but drop to random accuracy under voice conversion and editing attacks.
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HarmonicAttack: An Adaptive Cross-Domain Audio Watermark Removal
A black-box audio watermark removal attack trained on limited samples that generalizes across datasets and watermark schemes with high attack success rates.
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Feature-Aligned Speech Watermarking for Robustness to Reconstruction Distortions
Feature-aligned watermarking embeds a codec-generated pseudo-speech signal into the spectrogram to raise robustness against reconstruction models while keeping imperceptibility comparable to prior methods.
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XAttnMark: Learning Robust Audio Watermarking with Cross-Attention
XAttnMark is a new neural audio watermarking method using partial parameter sharing, cross-attention for message retrieval, temporal conditioning, and a psychoacoustic TF masking loss that reports state-of-the-art detection and attribution robustness.
- Who Gets Flagged? The Pluralistic Evaluation Gap in AI Content Watermarking