Despite their appealing flexibility, deep neural networks (DNNs) are vulnerable against
adversarial examples. Various adversarial defense strategies have been proposed to …

Recent studies have shown that deep neural networks (DNN) are vulnerable to adversarial
samples: maliciously-perturbed samples crafted to yield incorrect model outputs. Such …

Recent research has demonstrated that deep learning architectures are vulnerable to
adversarial attacks, high-lighting the vital need for defensive techniques to detect and …

Recent advances in adversarial Deep Learning (DL) have opened up a largely unexplored
surface for malicious attacks jeopardizing the integrity of autonomous DL systems. With the …

Deep Neural Networks (DNN) are vulnerable to adversarial perturbations—small changes
crafted deliberately on the input to mislead the model for wrong predictions. Adversarial …

Deep neural networks (DNNs) have been widely used in many fields due to their
increasingly high accuracy. However, they are also vulnerable to adversarial attacks, posing …

Several successful adversarial attacks have demonstrated the vulnerabilities of deep
learning algorithms. These attacks are detrimental in building deep learning based …

Deep Neural Networks (DNN) are vulnerable to adversarial samples that are generated by
perturbing correctly classified inputs to cause DNN models to misbehave (eg …

Deep neural networks are powerful and popular learning models that achieve state-of-the-
art pattern recognition performance on many computer vision, speech, and language …

Adversarial patch attacks create adversarial examples by injecting arbitrary distortions within
a bounded region of the input to fool deep neural networks (DNNs). These attacks are robust …