Synthetic Voice Spoofing Countermeasure Systems: Channel Effects Analysis

Synthetic Voice Spoofing Countermeasure Systems: Channel Effects Analysis

Introduction:

Synthetic voice spoofing, a cyberattack employing artificial intelligence and machine learning to mimic human voices, poses a serious threat. Countermeasure systems are crucial for detecting and preventing such attacks. This study investigates the effectiveness of these systems under various channel conditions.

Methodology:

We conducted an empirical study using a dataset containing synthetic voice spoofing attacks on multiple countermeasure systems. The dataset included recordings of synthetic voice attacks generated using diverse algorithms and models, played over different channels. We evaluated system performance in the presence of channel effects such as additive white Gaussian noise, reverberation, and frequency response distortion. We also compared the performance of different countermeasure types: feature-based, model-based, and hybrid systems.

Results:

Our findings indicate that channel effects significantly impact the performance of synthetic voice spoofing countermeasure systems. Reverberation and frequency response distortion negatively impacted the performance of all countermeasure systems. The type of countermeasure system also played a role. Feature-based systems performed well on clean audio but struggled with channel effects. Model-based systems showed better overall performance but were still affected by channel conditions. Hybrid systems, combining feature-based and model-based approaches, exhibited the best overall performance, effectively detecting spoofing attacks even in the presence of channel effects.

Conclusion:

Our study emphasizes the importance of considering channel effects when developing and evaluating synthetic voice spoofing countermeasure systems. Hybrid systems offer a promising solution for detecting and preventing synthetic voice spoofing attacks due to their ability to handle challenging channel conditions.