There’s a growing interest in employing autonomous mobile robots in open work environments such as warehouses, especially with the constraints posed by the global pandemic. And thanks to advances in deep learning algorithms and sensor technology, industrial robots are becoming more versatile and less costly.

But safety and security remain two major concerns in robotics.

… But adversarial training can have a significantly negative impact on the safety of robots, the researchers at IST Austria, MIT, and TU Wien discuss in a paper titled “Adversarial Training is Not Ready for Robot Learning.” Their paper, which has been accepted at the International Conference on Robotics and Automation (ICRA 2021), shows that the field needs new ways to improve adversarial robustness in deep neural networks used in robotics without reducing their accuracy and safety. Read More

#adversarial, #robotics

The dream of autonomous vehicles is that they can avoid human error and save lives, but a new European Union Agency for Cybersecurity (ENISA) report has found that autonomous vehicles are “highly vulnerable to a wide range of attacks” that could be dangerous for passengers, pedestrians, and people in other vehicles. Attacks considered in the report include sensor attacks with beams of light, overwhelming object detection systems, back-end malicious activity, and adversarial machine learning attacks presented in training data or the physical world.

“The attack might be used to make the AI ‘blind’ for pedestrians by manipulating for instance the image recognition component in order to misclassify pedestrians. This could lead to havoc on the streets, as autonomous cars may hit pedestrians on the road or crosswalks,” the report reads. “The absence of sufficient security knowledge and expertise among developers and system designers on AI cybersecurity is a major barrier that hampers the integration of security in the automotive sector.” Read More