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Mobile devices use facial recognition technology to help users quickly and securely unlock their phones, make a financial transaction or access medical records. But facial recognition technologies that employ a specific user-detection method are highly vulnerable to deepfake-based attacks that could lead to significant security concerns for users and applications, according to new research involving the Penn State College of Information Sciences and Technology.

The researchers found that most application programming interfaces that use facial liveness verification—a feature of facial recognition technology that uses computer vision to confirm the presence of a live user—don’t always detect digitally altered photos or videos of individuals made to look like a live version of someone else, also known as deepfakes. Applications that do use these detection measures are also significantly less effective at identifying deepfakes than what the app provider has claimed.

“In recent years we have observed significant development of facial authentication and verification technologies, which have been deployed in many security-critical applications,” said Ting Wang, associate professor of information sciences and technology and one principal investigator on the project. “Meanwhile, we have also seen substantial advances in deepfake technologies, making it fairly easy to synthesize live-looking facial images and video at little cost. We thus ask the interesting question: Is it possible for malicious attackers to misuse deepfakes to fool the facial verification systems?”

Scientists in Germany and the US have predicted the most topologically complex knot ever found in a protein using AlphaFold, the artificial intelligence (AI) system developed by Google’s DeepMind. Their complete analysis of the data produced by AlphaFold also revealed the first composite knots in proteins: topological structures containing two separate knots on the same string. If the discovered protein knots can be recreated experimentally it will serve to verify the accuracy of predictions made by AlphaFold.

Proteins can fold to form complex topological structures. The most intriguing of these are protein knots – shapes that would not disentangle if the protein were pulled from both ends. Peter Virnau, a theoretical physicist at Johannes Gutenberg University Mainz, tells Physics World that there are currently around 20 to 30 known knotted proteins. These structures, Virnau explains, raise interesting questions around how they fold and why they exist.

A protein’s shape can be closely linked with its function, but while there are a few theories on the functionality and purpose of protein knots there is little hard evidence to back these up. Virnau says that they might help to keep the proteins stable, by being particularly resistant to thermal fluctuations, for instance, but these are open questions. While protein knots are rare, they also appear to be highly preserved by evolution.

Nuclear power plants provide large amounts of electricity without releasing planet-warming pollution. But the expense of running these plants has made it difficult for them to stay open. If nuclear is to play a role in the U.S. clean energy economy, costs must come down. Scientists at the U.S. Department of Energy’s (DOE) Argonne National Laboratory are devising systems that could make nuclear energy more competitive using artificial intelligence.

Nuclear power plants are expensive in part because they demand constant monitoring and maintenance to ensure consistent power flow and safety. Argonne is midway through a $1 million, three-year project to explore how smart, computerized systems could change the economics.

“Operation and maintenance costs are quite relevant for nuclear units, which currently require large site crews and extensive upkeep,” said Roberto Ponciroli, a principal nuclear engineer at Argonne. “We think that autonomous operation can help to improve their profitability and also benefit the deployment of advanced reactor concepts.”

Soft robots that can complete tasks with high efficiency, accuracy and precision could have numerous valuable applications. For instance, they could be introduced in medical settings, helping doctors to carry out complex surgical procedures or assisting elderly and vulnerable patients during rehabilitation.

Soft robots are more flexible and can deform more. This can result in an increased dexterity (i.e., better manual skills when completing tasks), as well as in a reduction of payload (i.e., the robot capacity to carry a load), because they can produce smaller forces than rigid robotic systems.

Researchers at National University of Singapore and Beijing Jiaotong University have recently developed a new rod-driven soft robot (RDSR) that operates through push and pull movements. This robot, presented in a paper published in the IEEE Robotics and Automation Letters, combines the mechanisms of two robotic system previously created by members of the research group.

Food recalls could be a thing of the past if artificial intelligence (AI) is utilized in food production, according to a recent study from UBC and the University of Guelph.

The average cost of a food recall due to bacterial or microbial contamination, like E. coli, is US$10 million according to study co-author Dr. Rickey Yada, a professor and the dean of the UBC faculty of land and food systems.

We spoke with Dr. Yada about how AI can help optimize the current systems used in the food processing industry, and how it can help make our food supply safer.

Using a robot confined to a sphere, scientists have proven bodies can move freely in curved spaces without pushing against anything.

Booster 7 and Ship 24 both conducted two spin prime tests, Ship 25 was welded in the High Bay, and the Chopsticks were raised.

Video and Pictures from Mary (@BocaChicaGal), Nic (@NicAnsuini), and the NSF Robots.
Edited by Patrick Colquhoun(@Patrick_Colqu).

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