The use of AI to create image or video forgeries (deepfakes) represents a pervasive threat to the DoD. Indeed, both the National Defense Authorization Act (NDAA) of 2021 and the Identifying Outputs of Generative Adversarial Networks (IOGAN) Act of 2020 address the risk posed by deepfakes to national security. However, the barriers to creating photorealistic deepfakes are low: They can be created with a single, affordable gaming-type graphics processing unit with easily accessible and frequently maintained open source software.

Fortunately, the neural network architectures used to create deepfakes can also be engineered to detect them. AI detection algorithms have been developed that can identify manipulation not readily visible to the human eye, but no detector tool exists that can ingest multiple modes of media (image, video) and process them at scales required in DoD or other medium-to-large scale environments. 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.