Search Results for author:
Found 11 papers, 8 papers with code
Investigating the Adversarial Robustness of Density Estimation Using the Probability Flow ODE
Beyond their impressive sampling capabilities, score-based diffusion models offer a powerful analysis tool in the form of unbiased density estimation of a query sample under the training data distribution.
Robust Principles: Architectural Design Principles for Adversarially Robust CNNs
Our research aims to unify existing works' diverging opinions on how architectural components affect the adversarial robustness of CNNs.
RobArch: Designing Robust Architectures against Adversarial Attacks
Adversarial Training is the most effective approach for improving the robustness of Deep Neural Networks (DNNs).
Synthetic Dataset Generation for Adversarial Machine Learning Research
Existing adversarial example research focuses on digitally inserted perturbations on top of existing natural image datasets.
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data.
OpenFL: An open-source framework for Federated Learning
Federated learning (FL) is a computational paradigm that enables organizations to collaborate on machine learning (ML) projects without sharing sensitive data, such as, patient records, financial data, or classified secrets.
The Federated Tumor Segmentation (FeTS) Challenge
The goals of the FeTS challenge are directly represented by the two included tasks: 1) the identification of the optimal weight aggregation approach towards the training of a consensus model that has gained knowledge via federated learning from multiple geographically distinct institutions, while their data are always retained within each institution, and 2) the federated evaluation of the generalizability of brain tumor segmentation models "in the wild", i. e. on data from institutional distributions that were not part of the training datasets.
Toward Few-step Adversarial Training from a Frequency Perspective
We investigate adversarial-sample generation methods from a frequency domain perspective and extend standard $l_{\infty}$ Projected Gradient Descent (PGD) to the frequency domain.
Talk Proposal: Towards the Realistic Evaluation of Evasion Attacks using CARLA
In this talk we describe our content-preserving attack on object detectors, ShapeShifter, and demonstrate how to evaluate this threat in realistic scenarios.
Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation
Deep learning models for semantic segmentation of images require large amounts of data.
ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector
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) image classifier, as demonstrated in prior work.
