The advent of the Internet of Things (IoT) has brought forth an era of unprecedented
connectivity, with an estimated 80 billion smart devices expected to be in operation by the …

In a model inversion (MI) attack, an adversary abuses access to a machine learning (ML)
model to infer and reconstruct private training data. Remarkable progress has been made in …

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

EEG-based biometric systems verify the identity of a user by comparing the probe to a
reference EEG template of the claimed user enrolled in the system, or by classifying the …

Model inversion attacks (MIAs) aim to recover private data from inaccessible training sets of
deep learning models, posing a privacy threat. MIAs primarily focus on the white-box …

Industry 4.0 has witnessed the rise of complex robots fueled by the integration of Artificial
Intelligence/Machine Learning (AI/ML) and Digital Twin (DT) technologies. While these …

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) attacks pose a significant threat to the privacy of Deep Neural
Networks by recovering training data distribution from well-trained models. While existing …

This paper details the privacy and security landscape in today's cloud ecosystem and
identifies that there is a gap in addressing the risks introduced by machine learning models …

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 …