Abstract: Video anomaly detection (VAD) aims to discover behaviors or events deviating from the normality in videos. As a long-standing task in the field of computer vision, VAD has witnessed much ...

Deepfake faces generated via artificial intelligence (AI) have become so realistic that they routinely fool people, with some ...

These short anomaly-detection puzzles are designed to illustrate how reasoning often depends on identifying inconsistencies ...

Google Cloud has expanded its enterprise cybersecurity push with AI Threat Defense, as the market shifts towards AI-native threat detection, contextual triage, and supervised remediation. The launch ...

Apps that record visits are becoming popular, but they come with privacy and accuracy concerns. By Simar Bajaj At your next appointment, your doctor may have a new kind of assistant listening in: ...

The operation of fuel cell electric vehicle-to-grid (FCEV2G) stations presents a significant challenge due to the need to manage onsite hydrogen production, storage, and vehicle dispatch in volatile ...

Dr. James McCaffrey presents a complete end-to-end demonstration of anomaly detection using k-means data clustering, implemented with JavaScript. Compared to other anomaly detection techniques, ...

ABSTRACT: Video-based anomaly detection in urban surveillance faces a fundamental challenge: scale-projective ambiguity. This occurs when objects of different physical sizes appear identical in camera ...

In-line sensors, which are crucial for real-time (bio-) process monitoring, can suffer from anomalies. These signal spikes and shifts compromise process control. Due to the dynamic and non-stationary ...

Abstract: Anomaly detection (AD) is typically regarded as an unsupervised learning task, where the training data either do not contain any anomalous samples or contain only a few unlabeled anomalous ...