AI-Based Online API for Fake Speech Detection

Abstract

Deepfake audio technology poses a growing threat to information authenticity and in- tegrity. This thesis provides a systematic investigation of different Machine Learning

(ML) methods for detecting deepfake in Arabic speech. Firstly, a novel dataset of real and synthetic Arabic audio speech was created. Then, various ML methods were evaluated for their ability to discriminate between genuine and synthesized speech. Finally, a new Arabic deepfake speech framework is proposed, including handcrafted feature extraction and classification. Feature importance analysis revealed key acoustic and prosodic cues that contribute to the detection process, where the XGBoost classifier emerged as the most effective. Experimental results demonstrated the robustness and the high accuracy

of our proposed framework for Arabic deepfake speech detection compared to state-of-the- art methods. This research establishes a benchmark for Arabic deepfake audio detection

and contributes to the ongoing efforts to combat the harmful effects of this technology.