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

Deep learning models are known to be vulnerable to adversarial examples crafted by
adding human-imperceptible perturbations on benign images. Many existing adversarial …

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

Transferability of adversarial examples is of central importance for attacking an unknown
model, which facilitates adversarial attacks in more practical scenarios, eg, blackbox attacks …

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

The vulnerability of deep neural networks to adversarial examples has drawn tremendous
attention from the community. Three approaches, optimizing standard objective functions …

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 learning models are vulnerable to adversarial examples crafted by applying human-
imperceptible perturbations on benign inputs. However, under the black-box setting, most …

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) …

State-of-the-art deep neural networks are known to be vulnerable to adversarial examples,
formed by applying small but malicious perturbations to the original inputs. Moreover, the …