AI-Powered Arabic Deepfake Speech: Generation and Detection

AI-Powered Arabic Deepfake Speech: Generation and Detection

202011208 Marwan Abdullah Salem

202110609 Ammar Alshaik

202011679 Abdulaziz Sayed

The increasing sophistication of AI-generated speech has raised concerns over its misuse in voice impersonation scams and digital fraud. This project aims to develop a system capable of both generating and detecting deepfake Arabic speech to mitigate cybersecurity threats.

Utilizing Coqui XTTS-V2, a state-of-the-art text-to-speech (TTS) model, we generate high-fidelity Arabic speech samples. The training process relies on the Arabic Speech Corpus dataset, ensuring realism. To resolve potential misuse, we implement a deep learning-based detection model trained to differentiate between authentic and synthetic speech.

Preliminary results demonstrate the model’s effectiveness in classifying deepfake audio with high accuracy. Performance evaluation includes precision, recall, and overall detection rates, validating the system’s reliability. The findings underscore the need for enhanced detection mechanisms to address security risks posed by AI-driven voice synthesis.

This research contributes to digital forensics by offering a dual approach both generating and detecting deepfake speech to strengthen security measures. Future work will focus on refining real-time detection capabilities and expanding dataset coverage for improved robustness.