3 citations · 3 across the 1 of their papers we have counts for
4 papers
Toward Few-step Adversarial Training from a Frequency Perspective
Hans Shih-Han Wang, Cory Cornelius, Brandon Edwards +1
We investigate adversarial-sample generation methods from a frequency domain perspective and extend standard Projected Gradient Descent (PGD) to the frequency domain.…
Talk Proposal: Towards the Realistic Evaluation of Evasion Attacks using CARLA
Cory Cornelius, Shang-Tse Chen, Jason Martin +1
In this talk we describe our content-preserving attack on object detectors, ShapeShifter, and demonstrate how to evaluate this threat in realistic scenarios. We describe how we use…
Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation
Micah J Sheller, G Anthony Reina, Brandon Edwards +2
Deep learning models for semantic segmentation of images require large amounts of data. In the medical imaging domain, acquiring sufficient data is a significant challenge. Labelin…
ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector
Shang-Tse Chen, Cory Cornelius, Jason Martin +1
Given the ability to directly manipulate image pixels in the digital input space, an adversary can easily generate imperceptible perturbations to fool a Deep Neural Network (DNN) i…