66 citations · 108 across the 12 of their papers we have counts for
4 papers · 1 filter
Trainable Projected Gradient Method for Robust Fine-tuning
Junjiao Tian, Xiaoliang Dai, Chih-Yao Ma +3
Recent studies on transfer learning have shown that selectively fine-tuning a subset of layers or customizing different learning rates for each layer can greatly improve robustness…
Medical Aegis: Robust adversarial protectors for medical images
Qingsong Yao, Zecheng He, S. Kevin Zhou
Deep neural network based medical image systems are vulnerable to adversarial examples. Many defense mechanisms have been proposed in the literature, however, the existing defenses…
A Hierarchical Feature Constraint to Camouflage Medical Adversarial Attacks
Qingsong Yao, Zecheng He, Yi Lin +3
Deep neural networks (DNNs) for medical images are extremely vulnerable to adversarial examples (AEs), which poses security concerns on clinical decision making. Luckily, medical A…
Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detection
Qingsong Yao, Zecheng He, Hu Han +1
Recent methods in multiple landmark detection based on deep convolutional neural networks (CNNs) reach high accuracy and improve traditional clinical workflow. However, the vulnera…