Deepfake Voice Attacks: Detection Frameworks, Adversarial Robustness, And Ethical Implications

Emerging breakthroughs in deep learning have dramatically fastened the production of realistic audio deepfakes, consequently raising misinformation and fraud risks. In this paper, there is a complete overview and real-world implementation of sophisticated detection methods for speech deepfakes using Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and state-of-the-art hybrid models. We examine their strengths, weaknesses, and generalizability—especially under adversarial and real-world conditions—against baselines and dynamic datasets. The wider ethical, legal, and practical implications of introducing such systems are also discussed, placing this research at the leading edge of safe, interpretable, and generalizable deepfake detection research.