Robust Detection of Directional Adversarial Attacks in Deep Neural Networks for Radiological Imaging

Summary

Researchers propose a detection framework for adversarial attacks on deep neural networks used in radiological imaging. The method analyzes Sigmoid function variations and compares prediction differences between clean and attacked images, using random-noise attacks to identify adversarial perturbations. It reached up to 99.8% accuracy in identifying adversarial directional attacks across three medical datasets, including Chest X-Ray Images for Classification, Retinal Fundus Multi-disease Image Dataset and Brain Tumor Dataset.

Topics

• Adversarial machine learning

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