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Found 5 papers, 0 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.
Specifying Autonomous System Behaviour
Specifying the intended behaviour of autonomous systems is becoming increasingly important but is fraught with many challenges.
Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph
Establishing how a set of learners can provide privacy-preserving federated learning in a fully decentralized (peer-to-peer, no coordinator) manner is an open problem.
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.
