163 citations · 1.4k across the 139 of their papers we have counts for
4 papers · 2 filters
Interpolated Joint Space Adversarial Training for Robust and Generalizable Defenses
Chun Pong Lau, Jiang Liu, Hossein Souri +3
Adversarial training (AT) is considered to be one of the most reliable defenses against adversarial attacks. However, models trained with AT sacrifice standard accuracy and do not…
Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection
Jiang Liu, Alexander Levine, Chun Pong Lau +2
Object detection plays a key role in many security-critical systems. Adversarial patch attacks, which are easy to implement in the physical world, pose a serious threat to state-of…
Improving Deep Learning Interpretability by Saliency Guided Training
Aya Abdelsalam Ismail, Héctor Corrada Bravo, Soheil Feizi
Saliency methods have been widely used to highlight important input features in model predictions. Most existing methods use backpropagation on a modified gradient function to gene…
FOCUS: Familiar Objects in Common and Uncommon Settings
Priyatham Kattakinda, Soheil Feizi
Standard training datasets for deep learning often contain objects in common settings (e.g., "a horse on grass" or "a ship in water") since they are usually collected by randomly s…