Speech Watermarking with Discrete Intermediate Representations (2412.13917v1)

Abstract: Speech watermarking techniques can proactively mitigate the potential harmful consequences of instant voice cloning techniques. These techniques involve the insertion of signals into speech that are imperceptible to humans but can be detected by algorithms. Previous approaches typically embed watermark messages into continuous space. However, intuitively, embedding watermark information into robust discrete latent space can significantly improve the robustness of watermarking systems. In this paper, we propose DiscreteWM, a novel speech watermarking framework that injects watermarks into the discrete intermediate representations of speech. Specifically, we map speech into discrete latent space with a vector-quantized autoencoder and inject watermarks by changing the modular arithmetic relation of discrete IDs. To ensure the imperceptibility of watermarks, we also propose a manipulator model to select the candidate tokens for watermark embedding. Experimental results demonstrate that our framework achieves state-of-the-art performance in robustness and imperceptibility, simultaneously. Moreover, our flexible frame-wise approach can serve as an efficient solution for both voice cloning detection and information hiding. Additionally, DiscreteWM can encode 1 to 150 bits of watermark information within a 1-second speech clip, indicating its encoding capacity. Audio samples are available at https://DiscreteWM.github.io/discrete_wm.

• The paper introduces DiscreteWM, a speech watermarking framework embedding info in discrete representations via VQ-VAE for robustness against cloning.
• DiscreteWM employs a manipulator model for imperceptible watermark embedding and a robust detection mechanism involving a localizer and restorer to recover information reliably.
• Experiments show DiscreteWM achieves superior robustness and imperceptibility compared to others, offering high capacity (1-150 bps) and significantly faster detection (22.1x) even under distortions.

An Overview of "Speech Watermarking with Discrete Intermediate Representations"

This paper introduces DiscreteWM, a sophisticated framework for speech watermarking that aims to embed information in the discrete intermediate representations of speech. The motivation behind this work stems from the evolving threats associated with instant voice cloning technologies, which pose privacy risks by potentially misusing personal voice data. The authors argue that embedding watermarks into discrete latent spaces, as opposed to continuous spaces, brings about greater robustness and imperceptibility. The novelty of this approach lies in utilizing a vector-quantized autoencoder (VQ-VAE) for mapping speech into discrete latent spaces, then manipulating these spaces to embed watermark information through modular arithmetic operations on discrete token IDs.

Key Methodological Developments

1. Discrete Space Mapping: The framework leverages VQ-VAE to convert the speech into discrete intermediate representations, subsequently allowing watermarks to be injected by altering the modular arithmetic relations of discrete IDs. This method benefits from discrete encoding's inherent resistance to distortions, aligning with objectives of maximizing watermark robustness whilst minimizing perceptibility to humans.
2. Manipulator Model: To ensure that watermarks remain imperceptible, a manipulator model is employed. This model learns the distribution of the discrete speech tokens and selects optimal candidates for embedding watermarks. By aligning token IDs with the watermark message using modular arithmetic, the manipulator maintains the integrity of the original speech signal.
3. Robust Detection Mechanism: The paper presents a robust detection strategy by using a localizer to identify watermarked tokens and a restorer to recover watermark information. This two-pronged approach ensures that the parity of watermark-modified tokens can be reliably examined even under adverse conditions that introduce artifacts into the speech.

Experimental Findings

The experimental results affirm the effectiveness of DiscreteWM, showcasing superior performance in terms of both robustness and watermark imperceptibility compared to contemporary methods like WavMark and other traditional systems. By encoding between 1 to 150 bits per second of watermark information, DiscreteWM offers exceptional flexibility and high capacity encoding. Notably, its robustness remains unaffected by typical distortions such as Gaussian noise or MP3 compression, reaffirming the discrete latent representation's advantages.

Furthermore, DiscreteWM's frame-wise approach resolves the challenges surrounding fixed-length issues observed in other methods, enhancing the model's practicality for real-world applications. It achieves a notable detection speed enhancement, approximately 22.1 times faster than WavMark, proving valuable for AI-generated voice detection.

Implications and Future Directions

The development of DiscreteWM has significant theoretical and practical implications. Theoretically, it underscores the potential utility of discrete intermediate representations in enhancing system robustness and flexible content encoding. Practically, it provides a powerful tool against the misuse of voice cloning, increasing the security and accountability of AI-generated content through reliable detection mechanisms.

Looking ahead, the application of discrete intermediate representations can be further expanded. Future studies may explore optimizing manipulator models or exploring alternative quantization techniques that might further improve robustness and capacity. Moreover, as the landscape of AI and audio processing evolves, adaptations of frameworks like DiscreteWM can lead to more effective watermarking strategies, contributing substantially to the AI security domain.

In summary, this paper presents DiscreteWM as a robust and adaptable solution for speech watermarking, promising both secure usage and effective tracking of AI-generated audio content.