Deep Neural Networks (DNNs) have revolutionized various domains with their exceptional
performance across numerous applications. However, Model Inversion (MI) attacks, which …

Model Inversion (MI) attacks aim at leveraging the output information of target models to
reconstruct privacy-sensitive training data, raising widespread concerns on privacy threats of …

Abstract Model inversion (MI) attack reconstructs the private training data of a target model
given its output, posing a significant threat to deep learning models and data privacy. On …

Abstract Model Inversion (MI) attacks aim to reconstruct private training data by abusing
access to machine learning models. Contemporary MI attacks have achieved impressive …

The rapid adoption of deep learning in sensitive domains has brought tremendous benefits.
However, this widespread adoption has also given rise to serious vulnerabilities, particularly …

Tiny Machine Learning (TinyML) systems, which enable machine learning inference on
highly resource-constrained devices, are transforming edge computing but encounter …

The success of deep neural networks has driven numerous research studies and
applications from Euclidean to non-Euclidean data. However, there are increasing concerns …

The rapid progress of generative AI models has yielded substantial breakthroughs in AI,
facilitating the generation of realistic synthetic data across various modalities. However …

Prompt recovery in large language models (LLMs) is crucial for understanding how LLMs
work and addressing concerns regarding privacy, copyright, etc. The trend towards …

Poisoning attacks are a primary threat to machine learning models, aiming to compromise
their performance and reliability by manipulating training datasets. This paper introduces a …