DeepShield: Real-Time Multimodal Deepfake Detection via Lip-Sync, Pose Dynamics and Phonetic-Visual Consistency

DeepShield, a real-time multimodal deepfake detector based on lip-sync, pose dynamics and phonetic-visual consistency.

DeepShield, a real-time multimodal deepfake detector based on lip-sync, pose dynamics and phonetic-visual consistency. It is offered as a reference project: a complete, worked example of how a system like this is designed, built and evaluated, so that a developer can study it, run it and learn from it.

It suits learners who want to work with images or video: building a dataset, training or fine-tuning a vision model, measuring its performance and wrapping it in a usable tool.

The main parts are real-time processing of incoming data, fusion of more than one type of input, dataset preparation and annotation, model training or fine-tuning, and evaluation with standard metrics, plus visual examples. The reference build uses Apache Kafka, Python, OpenCV, PyTorch, and NumPy, with a clear project structure, setup instructions and sample data so that it can be run and extended.