A digital twin is an exact virtual copy of a real-world system. Built using real-time data, they provide a platform to test, simulate, and optimize the performance of their physical counterpart. In health care, medical digital twins can create virtual models of biological systems to predict diseases or test medical treatments. However, medical digital twins are susceptible to adversarial attacks, where small, intentional modifications to input data can mislead the system into making incorrect predictions, such as false cancer diagnoses, posing significant risks to the safety of patients.
Wavelet-based adversarial training: Cybersecurity system protects medical digital twins from attacks
