Explainable AI-Based Synthetic Media Detection Through Grounded LLM Explanation Using Evidence

An explainable AI-based synthetic media detection system that grounds its LLM explanation in evidence.

An explainable AI-based synthetic media detection system that grounds its LLM explanation in evidence. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on 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 explanations of each result (feature importance or visual evidence), dataset preparation and annotation, model training or fine-tuning, evaluation with standard metrics, plus visual examples, and an interface that works on uploaded images or live video. The reference build uses LLM APIs, SHAP, Python, OpenCV, and PyTorch, with a clear project structure, setup instructions and sample data so that it can be run and extended.