Deepfake audio poses a rising threat in communication platforms,
necessitating real-time detection for audio stream integrity. Unlike
traditional non-real-time approaches, this study assesses the viability of
employing static deepfake audio detection models in real-time communication
platforms. An executable software is developed for cross-platform
compatibility, enabling real-time execution. Two deepfake audio detection
models based on Resnet and LCNN architectures are implemented using the
ASVspoof 2019 dataset, achieving benchmark performances compared to ASVspoof
2019 challenge baselines. The study proposes strategies and frameworks for
enhancing these models, paving the way for real-time deepfake audio detection
in communication platforms. This work contributes to the advancement of audio
stream security, ensuring robust detection capabilities in dynamic, real-time
communication scenarios.

本研究旨在评估在实时通信平台上采用静态深假音频检测模型的可行性，研发了可在多平台上运行的可执行软件，并使用 ASVspoof 2019 数据集实现了基于 Resnet 和 LCNN 架构的两个深假音频检测模型，达到了与 ASVspoof 2019 挑战基准的比较性能。该研究提出了增强这些模型的策略和框架，为在通信平台上实现实时深假音频检测铺平了道路，从而推动音频流安全性的进一步发展，并确保在动态实时通信场景下具备强大的检测能力。