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Large-scale datasets and benchmarks for training, evaluating, and testing models to measure
Comprehensive Text Extraction and Document Processing Solutions for Users
This project focuses on detecting deepfake audio using advanced neural network architectures like VGG16, MobileNet, ResNet, and custom CNNs. It incorporates explainable AI (XAI) methods like LIME, Grad-CAM, and SHAP to enhance detection accuracy and provide insights into model predictions.