Deep neural networks are vulnerable to adversarial examples, which can mislead classifiers
by adding imperceptible perturbations. An intriguing property of adversarial examples is …

State-of-the-art deep neural network classifiers are highly vulnerable to adversarial
examples which are designed to mislead classifiers with a very small perturbation. However …

It is widely recognized that deep learning models lack robustness to adversarial examples.
An intriguing property of adversarial examples is that they can transfer across different …

Image classifiers based on deep neural networks suffer from harassment caused by
adversarial examples. Two defects exist in black-box iterative attacks that generate …

Deep neural networks are known to be extremely vulnerable to adversarial examples under
white-box setting. Moreover, the malicious adversaries crafted on the surrogate (source) …

Plentiful adversarial attack researches have revealed the fragility of deep neural networks
(DNNs), where the imperceptible perturbations can cause drastic changes in the output …

Deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples,
which can produce erroneous predictions by injecting imperceptible perturbations. In this …

Deep neural networks are vulnerable to adversarial examples that mislead the models with
imperceptible perturbations. Though adversarial attacks have achieved incredible success …

Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs)
based vision systems eg, for classification, segmentation and object detection. The …

While the transferability property of adversarial examples allows the adversary to perform
black-box attacks ie, the attacker has no knowledge about the target model), the transfer …