Deep Fake Defender: AI-Based Detection of Deepfake Voice Attacks in Real-Time Voice Authentication Systems

In recent years, advancements in artificial intelligence have led to the rapid development of deepfake technologies,

including hyper-realistic voice cloning. While voice authentication systems are increasingly adopted for secure access in

banking, smart devices, and enterprise systems, they remain vulnerable to deepfake audio attacks that can mimic a target’s voice

with alarming precision. This paper proposes an AI-powered framework for the real-time detection of deepfake voice samples in

authentication scenarios. The system employs deep learning models trained on both authentic and synthetic voice datasets to

analyze subtle acoustic features such as waveform anomalies, frequency inconsistencies, unnatural pauses, and generative noise

artifacts. Using tools like Librosa for audio feature extraction and Convolutional Neural Networks (CNNs) for classification, the

model achieves high accuracy in distinguishing real voices from AI-generated ones. The solution is designed to be lightweight

and compatible with existing voice authentication systems, enabling live screening during verification calls or voice logins. This

approach not only enhances the security of voice-based systems but also introduces a new defense layer against AI-enabled

social engineering attacks. The paper concludes with a discussion on future improvements, including multilingual support and

continuous model adaptation using unsupervised learning.