research
∙
06/22/2023
Visual Adversarial Examples Jailbreak Large Language Models
Recently, there has been a surge of interest in introducing vision into ...
share
research
∙
05/02/2023
Differentially Private In-Context Learning
An important question in deploying large language models (LLMs) is how t...
research
∙
02/03/2023
Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning
The proliferation of global censorship has led to the development of a p...
research
∙
08/20/2021
PatchCleanser: Certifiably Robust Defense against Adversarial Patches for Any Image Classifier
The adversarial patch attack against image classification models aims to...
research
∙
02/05/2021
DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding Attacks
State-of-the-art object detectors are vulnerable to localized patch hidi...
research
∙
06/24/2020
Time for a Background Check! Uncovering the impact of Background Features on Deep Neural Networks
With increasing expressive power, deep neural networks have significantl...
research
∙
05/17/2020
PatchGuard: Provable Defense against Adversarial Patches Using Masks on Small Receptive Fields
Localized adversarial patches aim to induce misclassification in machine...
research
∙
04/05/2020
FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning
This paper aims to enable training and inference of neural networks in a...
research
∙
12/10/2019
Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
research
∙
10/04/2018
A Query Tool for Efficiently Investigating Risky Software Behaviors
Advanced Persistent Threat (APT) attacks are sophisticated and stealthy,...
research
∙
02/18/2018
DARTS: Deceiving Autonomous Cars with Toxic Signs
Sign recognition is an integral part of autonomous cars. Any misclassifi...
research
∙
02/05/2018
