A Brief Study on Deepfake Detection and Prevention Approaches

Images, movies, and sounds produced using artificial intelligence (AI) technologies that depict events or things that do not exist in reality are known as Deepfakes. Large volumes of labeled training data are needed for deep learning models, which are frequently employed in AI-based Deepfake detection, to learn meaningful representations of real and modified material. The expanding complexity of AI-generated material has raised concerns about Deepfake prevention. The prevention of Deepfake technology abuse necessitates a multifaceted strategy that includes social, legal, and technological safeguards. In this paper describes a guideline for Deepfake detection and prevention methods, its supporting tools and specifies various datasets to solving different modalities.