Model extraction attacks (MEAs) allow adversaries to replicate a surrogate model analogous
to the target model's decision pattern. While several attacks and defenses have been …

In this paper, we study model inversion attribute inference (MIAI), a machine learning (ML)
privacy attack that aims to infer sensitive information about the training data given access to …

Many of today's machine learning (ML) systems are built by reusing an array of, often pre-
trained, primitive models, each fulfilling distinct functionality (eg, feature extraction). The …

Recent studies show that machine learning models are vulnerable to model extraction
attacks, where the adversary builds a substitute model that achieves almost the same …

Building advanced machine learning (ML) models requires expert knowledge and many
trials to discover the best architecture and hyperparameter settings. Previous work …

Emerging vulnerabilities in machine learning (ML) models due to adversarial attacks raise
concerns about their reliability. Specifically, evasion attacks manipulate models by …

Model extraction (ME) attacks represent one major threat to Machine-Learning-as-a-Service
(MLaaS) platforms by" stealing" the functionality of confidential machine-learning models …

Current model extraction attacks assume that the adversary has access to a surrogate
dataset with characteristics similar to the proprietary data used to train the victim model. This …

Inference attacks against Machine Learning (ML) models allow adversaries to learn
sensitive information about training data, model parameters, etc. While researchers have …

As a promising service, Machine Learning as a Service (MLaaS) provides personalized
inference functions for clients through paid APIs. Nevertheless, it is vulnerable to model …