Due to data dependency and model leakage properties, Deep Neural Networks (DNNs)
exhibit several security vulnerabilities. Several security attacks exploited them but most of …

Abstract Machine learning (ML), especially deep neural networks (DNNs) have been widely
used in various applications, including several safety-critical ones (eg autonomous driving) …

Black-box attack methods aim to infer suitable attack patterns to targeted DNN models by
only using output feedback of the models and the corresponding input queries. However …

Due to the excessive use of cloud-based machine learning (ML) services, the smart cyber-
physical systems (CPS) are increasingly becoming vulnerable to black-box attacks on their …

The vulnerability of deep neural network (DNN)-based systems makes them susceptible to
adversarial perturbation and may cause classification task failure. In this work, we propose …

Although state-of-the-art deep neural network models are known to be robust to random
perturbations, it was verified that these architectures are indeed quite vulnerable to …

While deep neural networks show unprecedented performance in various tasks, the
vulnerability to adversarial examples hinders their deployment in safety-critical systems …

Most of the data manipulation attacks on deep neural networks (DNNs) during the training
stage introduce a perceptible noise that can be catered by preprocessing during inference …

Deep neural networks (DNNs) have proven to be quite effective in a vast array of machine
learning tasks, with recent examples in cyber security and autonomous vehicles. Despite the …

Deep neural networks (DNNs) have been widely used in many fields such as images
processing, speech recognition; however, they are vulnerable to adversarial examples, and …