presented earlier this month, researchers from Multimedia and Information Security Lab in Drexel’s College of Engineering explained that they had created the “MISLnet algorithm” which can detect telltale signs of deepfake and manipulated pieces of media with incredible accuracy.The team trained the machine learning algorithm to extract and recognize digital “fingerprints” of many different video generators, such as Stable Video Diffusion, Video-Crafter, and Cog-Video.
the MISLnet algorithm represents a significant new milestone in detecting fake images and video content. That’s because many of the “digital breadcrumbs” that existing systems look for in regular digitally edited media are not present in entirely AI-generated media. “Until now, forensic detection programs have been effective against edited videos by simply treating them as a series of images and applying the same detection process,” Stamm adds.
As 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.reports that the team’s new MISLnet algorithm has been trained 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.
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