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Found 4 papers, 1 papers with code
Compilation as a Defense: Enhancing DL Model Attack Robustness via Tensor Optimization
Adversarial Machine Learning (AML) is a rapidly growing field of security research, with an often overlooked area being model attacks through side-channels.
Model Leeching: An Extraction Attack Targeting LLMs
Model Leeching is a novel extraction attack targeting Large Language Models (LLMs), capable of distilling task-specific knowledge from a target LLM into a reduced parameter model.
PINCH: An Adversarial Extraction Attack Framework for Deep Learning Models
Adversarial extraction attacks constitute an insidious threat against Deep Learning (DL) models in-which an adversary aims to steal the architecture, parameters, and hyper-parameters of a targeted DL model.
HUNTER: AI based Holistic Resource Management for Sustainable Cloud Computing
The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for hosting application services on the cloud.
