  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.
