Latent-Mark: An Audio Watermark Robust to Neural Resynthesis
Abstract
While existing audio watermarking techniques have achieved strong robustness against traditional digital signal processing (DSP) attacks, they remain vulnerable to neural resynthesis. This occurs because modern neural audio codecs act as semantic filters and discard the imperceptible waveform variations used in prior watermarking methods. To address this limitation, we propose Latent-Mark, the first zero-bit audio watermarking framework designed to survive semantic compression. Our key insight is that robustness to the encode-decode process requires embedding the watermark within the codec's invariant latent space. We achieve this by optimizing the audio waveform to induce a detectable directional shift in its encoded latent representation, while constraining perturbations to align with the natural audio manifold to ensure imperceptibility. To prevent overfitting to a single codec's quantization rules, we introduce Cross-Codec Optimization, jointly optimizing the waveform across multiple surrogate codecs to target shared latent invariants. Extensive evaluations demonstrate robust zero-shot transferability to unseen neural codecs, achieving state-of-the-art resilience against traditional DSP attacks while preserving perceptual imperceptibility. Our work inspires future research into universal watermarking frameworks capable of maintaining integrity across increasingly complex and diverse generative distortions.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of Latent-Mark, a novel zero-bit audio watermarking framework that effectively survives neural resynthesis by embedding watermarks within the latent space of audio codecs. This work represents a meaningful advancement in audio watermarking, addressing vulnerabilities posed by modern neural codecs and providing a foundation for future research in universal watermarking techniques.
Methodology
The methodology presented in Latent-Mark is innovative, leveraging the concept of embedding watermarks within the invariant latent space of neural audio codecs. The approach of optimizing the audio waveform to induce a detectable shift while ensuring imperceptibility is a significant advancement over traditional methods. The introduction of Cross-Codec Optimization is particularly noteworthy, as it addresses the challenge of overfitting to specific codec characteristics, enhancing the generalizability of the watermarking technique across different audio codecs.
Results & evaluation
The paper provides extensive evaluations demonstrating the robustness of the proposed method against both traditional DSP attacks and neural resynthesis. The experiments are well-structured, showcasing the performance of Latent-Mark in various scenarios, including zero-shot transferability to unseen codecs. The results indicate a strong resilience to attacks while maintaining perceptual quality, which is crucial for practical applications.
Reproducibility
The paper lacks detailed implementation specifics, such as code availability or datasets used for training and evaluation, which could hinder reproducibility. Providing a GitHub repository or links to datasets would significantly enhance the reproducibility of the results.
Limitations
One limitation of the study is the potential dependency on the specific codecs chosen for Cross-Codec Optimization. While the method shows promise, its performance on a broader range of codecs, especially those not included in the training phase, remains to be fully explored. Additionally, the paper does not address the computational complexity of the optimization process, which could impact real-time applications.
Broader impact
The implications of this research are significant, as it opens avenues for secure audio transmission and copyright protection in an era where neural codecs are becoming prevalent. The ability to maintain watermark integrity against advanced generative models could have far-reaching applications in media, entertainment, and digital rights management. The main contribution of this paper is the introduction of Latent-Mark, a novel zero-bit audio watermarking framework that effectively survives neural resynthesis by embedding watermarks within the latent space of audio codecs. This work represents a meaningful advancement in audio watermarking, addressing vulnerabilities posed by modern neural codecs and providing a foundation for future research in universal watermarking techniques.
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