Deep neural networks (DNN)-based machine learning (ML) algorithms have recently
emerged as the leading ML paradigm particularly for the task of classification due to their …

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

We propose a new real-world attack against the computer vision based systems of
autonomous vehicles (AVs). Our novel Sign Embedding attack exploits the concept of …

Deep learning has been widely used in the field of image classification and image
recognition and achieved positive practical results. However, in recent years, a number of …

This article provides a detailed survey of the so-called adversarial attacks and defenses.
These are special modifications to the input data of machine learning systems that are …

Studies have proven that applying patch stickers generated through adversarial training to
target objects can effectively deceive classifiers or target detectors. These'Print-and …

Correct environmental perception of objects on the road is vital for the safety of autonomous
driving. Making appropriate decisions by the autonomous driving algorithm could be …

Despite the high quality performance of the deep neural network in real-world applications,
they are susceptible to minor perturbations of adversarial attacks. This is mostly …

In smart cities, connected and automated surveillance systems play an essential role in
ensuring safety and security of life, property, critical infrastructures and cyber-physical …

Deep neural networks (DNNs) are found to be vulnerable against adversarial examples,
which are carefully crafted inputs with a small magnitude of perturbation aiming to induce …