Deep Learning is currently used to perform multiple tasks, such as object recognition, face
recognition, and natural language processing. However, Deep Neural Networks (DNNs) are …

Unrestricted adversarial attacks typically manipulate the semantic content of an image (eg,
color or texture) to create adversarial examples that are both effective and photorealistic …

Computer vision applications like traffic monitoring, security checks, self-driving cars,
medical imaging, etc., rely heavily on machine learning models. It raises an essential …

Adversarial examples mislead deep neural networks with imperceptible perturbations and
have brought significant threats to deep learning. An important aspect is their transferability …

Recent advances in instruction tuning have led to the development of State-of-the-Art Large
Multimodal Models (LMMs). Given the novelty of these models the impact of visual …

Many works have shown that the adversarial examples being generated on a known
substitute model have the ability to mislead other unknown black-box models, which has …

Abstract Deep Neural Networks (DNNs) are vulnerable to adversarial examples, which are
crafted by adding human-imperceptible perturbations to the benign inputs. Simultaneously …

Deep neural networks have been found to be vulnerable in a variety of tasks. Adversarial
attacks can manipulate network outputs, resulting in incorrect predictions. Adversarial …

Adversarial machine learning attacks on video action recognition models is a growing
research area and many effective attacks were introduced in recent years. These attacks …

Although deep neural networks (DNNs) have achieved excellent performance on
hyperspectral image (HSI) classification tasks, their robustness is threatened by carefully …