Software Engineering Institute

The aim of this project is to develop a deepfake detection prototype framework that incorporates open source and novel detector algorithms capable of detecting at least three types of AI artifacts per mode with at least 85% accuracy, for both image and video modes.

The initial phase of our work consisted of identifying and testing state-of-the-art, open source deepfake detector algorithms and selecting those that perform the best on open source data. We plan to incorporate these algorithms into a prototype tool that can ingest each mode of data (image and video) at scale, perform filtering and triaging relevant to a specific type of media (e.g., images of human faces, text, videos containing human subjects), and assign a probability of that media having been manipulated with AI. We will work to determine the best way to combine individual model scores into a robust, high-integrity performance metric. The goal of these efforts is a working prototype capable of ingesting all modes of data and able to detect at least three types of AI artifacts for each mode with at least 85% accuracy.