Robust Detection of Directional Adversarial Attacks in Deep Neural Networks for Radiological Imaging
Deep learning is now central to radiology, helping detect changes on X-rays, CTs, and MRIs. However, these systems are highly vulnerable to adversarial attacks - small, crafted perturbations that mislead models while appearing unchanged to humans. Such errors risk missed cancers or false positives. Studies show over 90% attack success on medical scans. Almost 30% of US hospitals use AI in imaging in 2023. Thus, an effective method of detecting such attacks and their directionality is crucial to ensure the credibility and reliability of medical systems based on DNNs. We propose a novel detection framework that leverages subsequent attacks using random noise to identify adversarial perturba...
| Authors | Tristan Malatyński, Joanna Jaworek-Korjakowska |
|---|---|
| Primary category | cs.CV |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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