With the development of adversarial generative networks, generating fake media content has become easily accessible to the general public, especially through the emergence of free software tools. In this project, we aim to develop a web platform for detecting counterfeit media content in image, video and audio files. We will analyze various features for each type of content (image, video, audio), such as artifacts, PRNU signatures, facial expression, speech and lip-sync correspondence, etc., and then develop artificial intelligence (AI)-based systems to detect such features automatically. We will consider developing efficient models that ensure near-real-time operation, as well as compatibility with various file formats and protocols for live streams. The developed systems will be integrated into a suite of web services, which will in turn be used by a user-friendly web application. For proper testing of the AI-based models, we will create 3 test datasets with counterfeit media content, and a web application for generating such content. Finally, we will integrate all developed components (models, services, web applications, databases) into a unified platform.