Deepfake technology has advanced swiftly, enabling the rapid production of hyper-realistic synthetic media that pose considerable threats to digital security, privacy, military operations, and information integrity. This paper extensively examines visual intelligence and computer vision methodologies for deepfake detection, covering recent developments in deep learning, adversarial strategies, and feature extraction. It reviews prevalent generation architectures—including GANs, autoencoders, neural rendering, and diffusion models—alongside novel adversarial tactics that enhance realism while evading detection, particularly in military and intelligence contexts. We also investigate visual... More >

Falls are a major cause of injury, hospitalization, and loss of independence among older adults, spurring interest in visual intelligence-based automated fall detection for timely response and continuous monitoring. This article presents a systematic review of such systems, focusing on YOLO-based approaches. Following PRISMA guidelines, the review covers 2016–2025 literature, identifying 637 records and including 63 studies after screening. We examine datasets, preprocessing strategies, evaluation protocols, metrics, and hardware platforms, comparing reported accuracy, efficiency, and real-time feasibility across different designs. Evidence is strongest for YOLOv3 through YOLOv9, while evi... More >