SBIR-STTR Award

To provide a secure, cloud-based “deepfake” detection system that identifies synthetic human images, fake identifies, deceptive profiles, and cloned voice and speech based on trusted, explainable artificial intelligence (AI) and machine learning (ML) algorithms. We will construct a parsing and characterization engine that can learn and perceive computer-mediated deception and develop a training program and platform to facilitate United States Air Force (USAF) personnel to identify certain forms of deception in multi-modal online media context. Multiple formats, including text, image, and video will be analyzed and assessed.

The objective of the proposed work is to build an image forensics software tool that detects adversarial data manipulation of images. In Phase II a Convolutional Neural Network will be built to identify additions, subtractions, and modifications to objects in images. Novel elements include the architecture element of an attention network, incorporated to aid the Network in focusing in on relevant elements of an image, much like humans focus in on relevant details while ignoring irrelevant or consistent stimuli. The Network will take in images and return a prediction or likelihood that the image has been modified, as well as a prediction of where and what kind of modification has occurred. Types of modifications on which the Network will be trained include splicing, where parts of one image are copied into another; copy-move, where part of an image is copied onto a different part of the same image; and removal, where part of an image is removed and the background is filled in behind it. A ready-for-use software tool encompassing the Network and a user interface will be delivered at the end of Phase II.