RTCFake: Speech Deepfake Detection in Real-Time Communication
Abstract
With the rapid advancement of speech generation technologies, the threat posed by speech deepfakes in real-time communication (RTC) scenarios has intensified. However, existing detection studies mainly focus on offline simulations and struggle to cope with the complex distortions introduced during RTC transmission, including unknown speech enhancement processes (e.g., noise suppression) and codec compression. To address this challenge, we present the first large-scale speech deepfake dataset tailored for RTC scenarios, termed \textit{RTCFake}, totaling approximately 600 hours. The dataset is constructed by transmitting speech through multiple mainstream social media and conferencing platforms (e.g., Zoom), enabling precise pairing between offline and online speech. In addition, we propose a phoneme-guided consistency learning (PCL) strategy that enforces models to learn platform-invariant semantic structural representations. In this paper, the RTCFake dataset is divided into training, development, and evaluation sets. The evaluation set further includes both unseen RTC platforms and unseen complex noise conditions, thereby providing a more realistic and challenging evaluation benchmark for speech deepfake detection. Furthermore, the proposed PCL strategy achieves significant improvements in both cross-platform generalization and noise robustness, offering an effective and generalizable modeling paradigm. The \textit{RTCFake} dataset is provided in the {https://huggingface.co/datasets/JunXueTech/RTCFake}.
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 paper presents RTCFake, a novel dataset and a phoneme-guided consistency learning strategy for detecting speech deepfakes in real-time communication, addressing a critical gap in existing research. The methodology is innovative, and the experimental results demonstrate substantial improvements, making it a valuable contribution to the field of audio and speech processing.
Methodology
The paper introduces a phoneme-guided consistency learning (PCL) strategy, which is a novel approach aimed at enhancing the robustness of speech deepfake detection in real-time communication scenarios. The proposed methodology effectively addresses the challenges posed by various distortions and codec compressions encountered in RTC environments. The dataset, RTCFake, is a significant contribution, as it is specifically designed for the complexities of real-time communication, which is often overlooked in existing literature.
Results & evaluation
The authors provide a comprehensive evaluation of their proposed method using a large-scale dataset of approximately 600 hours of speech. The evaluation set includes both unseen RTC platforms and complex noise conditions, which enhances the realism of the testing environment. The reported improvements in cross-platform generalization and noise robustness are significant, indicating that the proposed method is effective in practical applications.
Reproducibility
While the paper mentions the availability of the RTCFake dataset on Hugging Face, it lacks detailed implementation specifics regarding the PCL strategy and the models used. This omission could hinder reproducibility, as other researchers may struggle to replicate the results without clear guidance on the experimental setup.
Limitations
One limitation is that the dataset may not encompass all possible real-time communication scenarios, potentially limiting the generalizability of the findings. Additionally, the paper does not address the computational efficiency of the proposed method, which is crucial for real-time applications.
Broader impact
The implications of this research are significant, as it addresses a pressing issue in the age of deepfake technology. The ability to detect speech deepfakes in real-time communication can have far-reaching effects on security, privacy, and trust in digital communications. The proposed dataset and methodology could serve as a foundation for future research in this area. The paper presents RTCFake, a novel dataset and a phoneme-guided consistency learning strategy for detecting speech deepfakes in real-time communication, addressing a critical gap in existing research. The methodology is innovative, and the experimental results demonstrate substantial improvements, making it a valuable contribution to the field of audio and speech processing.
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