Detecting Deepfakes: Exploring Machine Learning Models for Audio, Video, and Image Analysis
Keywords:
Deepfake Detection, Deepfake Technology Evolution, Ensemble Learning, Machine Learning Techniques, Performance AnalysisAbstract
The rapid evolution of deepfake technology has created substantial hurdles for the detection of altered media. This study investigates the field of deepfake detection with an emphasis on the use of machine learning techniques in the fields of image, video, and audio analysis. The effectiveness of several machine learning models—Random Forests, Gradient Boosting Machines, Support Vector Machines, Neural Networks, and Convolutional Neural Networks, among others—in identifying deepfakes is compared and contrasted. The analysis outlines the benefits and drawbacks of each model and offers performance insights derived from real-world case studies and research findings. The paper also addresses recent developments in deepfake detection techniques, including ensemble learning approaches and ResNet topologies, which present interesting directions for further research and development in the fight against the spread of manipulated media.
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