In recent years, DeepFake technology has achieved unprecedented success in high-quality video synthesis, but these methods also pose potential and severe security threats to humanity. DeepFake can be bifurcated into entertainment applications like face swapping and illicit uses such as lip-syncing fraud. However, lip-forgery videos, which neither change identity nor have discernible visual artifacts, present a formidable challenge to existing DeepFake detection methods. Our preliminary experiments have shown that the effectiveness of the existing methods often drastically decrease or even fail when tackling lip-syncing videos.In this paper, for the first time, we propose a novel approach dedicated to lip-forgery identification that exploits the inconsistency between lip movements and audio signals. We also mimic human natural cognition by capturing subtle biological links between lips and head regions to boost accuracy.

Sarah Vitak: This is Scientific American's 60 Second Science. Early last year a TikTok of Tom Cruise doing a magic trick went viral. I mean, it's all the real thing."] Matt Groh: A deepfake is a video where an individual's face has been altered by a neural network to make an individual do or say something that the individual has not done or said. Vitak: That is Matt Groh, a Ph.D. student and researcher at the M.I.T. Media Lab. Groh: It seems like there's a lot of anxiety and a lot of worry about deepfakes and our inability to, you know, know the difference between real or fake. Vitak: But he points out that the videos posted on the Deep Tom Cruise account aren't your standard deepfakes. The creator, Chris Umé, went back and edited individual frames by hand to remove any mistakes or flaws left behind by the algorithm. It takes him about 24 hours of work for each 30-second clip. It makes the videos look eerily realistic. But without that human touch, a lot of flaws show up in ...

All those topics have made headlines this year--in Education Week and elsewhere--and all of them are splashed over the agenda of the country's largest education technology conference, which kicks off in Philadelphia this weekend. The International Society for Technology in Education will draw thousands of teachers, school administrators, and researchers from across the world, not to mention the dozens of ed-tech companies hungry for a piece of the K-12 market. Ben Herold, an ISTE veteran, will be moderating a panel on meeting the ed tech needs of extraordinary students. And I'll be at ISTE for the first time! Follow me on Twitter at @AlysonRKlein.

A new MIT-developed technique enables robots to quickly identify objects hidden in a three-dimensional cloud of data, reminiscent of how some people can make sense of a densely patterned "Magic Eye" image if they observe it in just the right way. Robots typically "see" their environment through sensors that collect and translate a visual scene into a matrix of dots. Think of the world of, well, "The Matrix," except that the 1s and 0s seen by the fictional character Neo are replaced by dots -- lots of dots -- whose patterns and densities outline the objects in a particular scene. Conventional techniques that try to pick out objects from such clouds of dots, or point clouds, can do so with either speed or accuracy, but not both. With their new technique, the researchers say a robot can accurately pick out an object, such as a small animal, that is otherwise obscured within a dense cloud of dots, within seconds of receiving the visual data.

How do you tell just how smart your robot is? Simple: give it a universal IQ test. Traditional measures of human intelligence often won't be appropriate for systems that have senses, environments and cognitive capacities very different from our own. So Shane Legg and Marcus Hutter at the Swiss Institute for Artificial Intelligence in Manno-Lugano have drafted an alternative test that will allow the intelligence of vision systems, robots, natural language processing programs or trading agents to be compared and contrasted despite their broad and disparate functions. Although there is no consensus on what exactly human intelligence is, most views appear to cluster around the idea that it hinges on a general ability to achieve goals in a wide range of environments, says Legg. The same can be applied to an AI system, by measuring its ability to carry out complex tasks within its particular environment, compared with all other environments.