Emerging Distributed AI systems are revolutionizing big data computing and data
processing capabilities with growing economic and societal impact. However, recent studies …

Deep neural network ensembles hold the potential of improving generalization performance
for complex learning tasks. This paper presents formal analysis and empirical evaluation to …

State-of-the-art (SOTA) adversarial attacks expose vulnerabilities in object detectors, often
resulting in erroneous predictions. However, existing adversarial attacks neglect the stealth …

Despite their many advantages and positive features, the deep neural networks are
extremely vulnerable against adversarial attacks. This drawback has substantially reduced …

Federated learning (FL) enables decentralized training of deep neural networks (DNNs) for
object detection over a distributed population of clients. It allows edge clients to keep their …

Many recent studies have shown that deep neural models are vulnerable to adversarial
samples: images with imperceptible perturbations, for example, can fool image classifiers. In …

Tracking multiple objects in a continuous video stream is crucial for many computer vision
tasks. It involves detecting and associating objects with their respective identities across …

Deep neural networks (DNNs) are vulnerable to adversarial attacks. In particular, object
detectors may be attacked by applying a particular adversarial patch to the image. However …

The adversarial perturbation for object detectors has drawn increasing attention due to the
application in video surveillance and autonomous driving. However, few works have …

Recent works have shown that deep learning models are highly vulnerable to adversarial
examples, limiting the application of deep learning in security-critical systems. This paper …