11 citations · 34 across the 11 of their papers we have counts for
3 papers · 2 filters
Double Targeted Universal Adversarial Perturbations
Philipp Benz, Chaoning Zhang, Tooba Imtiaz +1
Despite their impressive performance, deep neural networks (DNNs) are widely known to be vulnerable to adversarial attacks, which makes it challenging for them to be deployed in se…
Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy
Philipp Benz, Chaoning Zhang, Adil Karjauv +1
Convolutional neural networks (CNNs) have made significant advancement, however, they are widely known to be vulnerable to adversarial attacks. Adversarial training is the most wid…
Batch Normalization Increases Adversarial Vulnerability and Decreases Adversarial Transferability: A Non-Robust Feature Perspective
Philipp Benz, Chaoning Zhang, In So Kweon
Batch normalization (BN) has been widely used in modern deep neural networks (DNNs) due to improved convergence. BN is observed to increase the model accuracy while at the cost of…