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Found 8 papers, 0 papers with code
Knowledge Distillation-Empowered Digital Twin for Anomaly Detection
However, constructing a DT for anomaly detection in TCMS necessitates sufficient training data and extracting both chronological and context features with high quality.
Simultaneous Adversarial Attacks On Multiple Face Recognition System Components
In this work, we investigate the potential threat of adversarial examples to the security of face recognition systems.
Advancing Deep Metric Learning Through Multiple Batch Norms And Multi-Targeted Adversarial Examples
Deep Metric Learning (DML) is a prominent field in machine learning with extensive practical applications that concentrate on learning visual similarities.
Powerful Physical Adversarial Examples Against Practical Face Recognition Systems
Notably, our smoothness loss results in a 1. 17 and 1. 97 times better mean attack success rate (ASR) in physical white-box and black-box attacks, respectively.
Anomaly Detection using Capsule Networks for High-dimensional Datasets
To the best of our knowledge, this is the first instance where a capsule network is analyzed for the anomaly detection task in a high-dimensional complex data setting.
On Brightness Agnostic Adversarial Examples Against Face Recognition Systems
This paper introduces a novel adversarial example generation method against face recognition systems (FRSs).
Dodging Attack Using Carefully Crafted Natural Makeup
Deep learning face recognition models are used by state-of-the-art surveillance systems to identify individuals passing through public areas (e. g., airports).
Evaluating the Cybersecurity Risk of Real World, Machine Learning Production Systems
Using the extension, security practitioners can apply attack graph analysis methods in environments that include ML components; thus, providing security practitioners with a methodological and practical tool for evaluating the impact and quantifying the risk of a cyberattack targeting an ML production system.
