Dongguk University researchers develop wavelet-based adversarial training: a breakthrough defense system for medical digital twins
Peer-Reviewed Publication
Updates every hour. Last Updated: 17-Aug-2025 12:11 ET (17-Aug-2025 16:11 GMT/UTC)
Medical digital twins are virtual models of the human body that can help predict diseases with high accuracy. However, they are vulnerable to cyberattacks that can manipulate data and lead to incorrect diagnoses. To address this, researchers from Dongguk University developed the Wavelet-Based Adversarial Training (WBAD) defense system. Tested on a breast cancer diagnostic model, WBAD restored accuracy to 98% against attacks, ensuring safer and more reliable medical digital twins for healthcare applications.
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In 1962, when environmentalist and author Rachel Carson penned "Silent Spring," alerting the world to the dangers of the pesticide DDT, it was the reproductive threat to birds – the bald eagle in particular – that spurred people to action.
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