University of Tebessa

AI-Based Online API for Fake Speech Detection

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dc.contributor.author BOUSBA, Abdelsamie
dc.date.accessioned 2024-10-15T10:14:25Z
dc.date.available 2024-10-15T10:14:25Z
dc.date.issued 2024-07-14
dc.identifier.uri http//localhost:8080/jspui/handle/123456789/12102
dc.description.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. en_US
dc.language.iso en en_US
dc.publisher University Larbi Tébessi – Tébessa en_US
dc.subject Deepfake Audio, Arabic Speech, Ensemble Learning, Machine Learning, Deep Learning, Generative Artificial Intelligence. en_US
dc.title AI-Based Online API for Fake Speech Detection en_US
dc.type Thesis en_US


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