Neural networks are easily disrupted by adversarial attacks. Scientists at the University of Würzburg and the Technical University of Munich are now developing new methods to make these systems more robust.
Cars that move independently through traffic; software that recognizes malignant changes in the lungs on X-ray images; chat bots that pass demanding entrance exams at US universities: In recent years, numerous applications based on the principles of "machine learning" have found their way into the everyday lives of many people.
Despite their great success, however, these systems too often have a significant weakness: They can easily be thrown off course by adversarial attacks. ...
