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Employing Quantum Machine Learning for Improved Deepfake Audio Detection
This project aims to research and develop on building a real-time deepfake audio detection system that is capable of distinguishing between authentic and spoofed audio voices with the help of Quantum Machine Learning (QML). The primary goal is to identify the limitations of existing classical ML techniques and explore how QML can improve the different existing challenges like processing big data, loading large complex models, improving generalization and detecting new forms of deepfakes.
Faculty Supervisor:
Ajmery Sultana
Student:
Partner:
World University of Bangladesh
Discipline:
Computer science
Sector:
Cyber Security; Quantum Science; Entertainment and Media
University:
Algoma University
Program:
Globalink Research Award
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