DEVELOPMENT OF A DEEPFAKE DETECTION TOOL USING A MACHINE LEARNING ALGORITHM
Abstract
The proliferation of deepfake technology presents a significant challenge to digital media integrity and trust. In response, this study aims to develop an advanced deepfake detection tool using machine learning algorithms. Leveraging primary data collection methods such as surveys and interviews, the research explores user perceptions, challenges, and requirements for effective detection. The system architecture integrates feature extraction, classification models, and user-friendly interfaces to accurately identify manipulated multimedia content. Evaluation metrics including accuracy, precision, and recall are employed to assess system performance. Through this endeavor, the study seeks to contribute to the ongoing efforts to combat the harmful effects of synthetic media manipulation and preserve digital authenticity and trust.
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