Search Results for author:
Found 8 papers, 3 papers with code
What Was Your Prompt? A Remote Keylogging Attack on AI Assistants
In this paper, we unveil a novel side-channel that can be used to read encrypted responses from AI Assistants over the web: the token-length side-channel.
SoK: Reducing the Vulnerability of Fine-tuned Language Models to Membership Inference Attacks
We provide the first systematic review of the vulnerability of fine-tuned large language models to membership inference attacks, the various factors that come into play, and the effectiveness of different defense strategies.
Transpose Attack: Stealing Datasets with Bidirectional Training
In addition, in this work we show that neural networks can be taught to systematically memorize and retrieve specific samples from datasets.
YolOOD: Utilizing Object Detection Concepts for Multi-Label Out-of-Distribution Detection
In this research, we propose YolOOD - a method that utilizes concepts from the object detection domain to perform OOD detection in the multi-label classification task.
The Security of Deep Learning Defences for Medical Imaging
Deep learning has shown great promise in the domain of medical image analysis.
Fair and accurate age prediction using distribution aware data curation and augmentation
One of the main causes of unfair behavior in age prediction methods lies in the distribution and diversity of the training data.
FOOD: Fast Out-Of-Distribution Detector
Deep neural networks (DNNs) perform well at classifying inputs associated with the classes they have been trained on, which are known as in distribution inputs.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
GIM: Gaussian Isolation Machines
In many cases, neural network classifiers are likely to be exposed to input data that is outside of their training distribution data.
