RTCFake: Speech Deepfake Detection in Real-Time Communication

TL;DR

This paper introduces RTCFake, a large-scale speech deepfake dataset for real-time communication, and proposes a phoneme-guided consistency learning strategy to improve detection robustness across platforms and noise conditions.

Contribution

It presents the first RTC-specific speech deepfake dataset and a novel PCL method that enhances cross-platform and noise-robust detection performance.

Findings

RTCFake dataset contains approximately 600 hours of data from multiple platforms.

PCL strategy significantly improves cross-platform generalization.

The approach enhances robustness against complex noise and unknown speech enhancement processes.

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}.