The latest in what will be a continuing arms race between creating and detecting videos:

The new tool the research project is unleashing on deepfakes, called “MISLnet”, evolved from years of data derived from detecting fake images and video with tools that spot changes made to digital video or images. These may include the addition or movement of pixels between frames, manipulation of the speed of the clip, or the removal of frames.

Such tools work because a digital camera’s algorithmic processing creates relationships between pixel color values. Those relationships between values are very different in user-generated or images edited with apps like Photoshop.

But because AI-generated videos aren’t produced by a camera capturing a real scene or image, they don’t contain those telltale disparities between pixel values.

The Drexel team’s tools, including MISLnet, learn using a method called a constrained neural network, which can differentiate between normal and unusual values at the sub-pixel level of images or video clips, rather than searching for the common indicators of image manipulation like those mentioned above.

Research paper.