Although deep neural networks (DNNs) have achieved tremendous performance in diverse
vision challenges, they are surprisingly susceptible to adversarial examples, which are born …

Deep neural network (DNN) as a popular machine learning model is found to be vulnerable
to adversarial attack. This attack constructs adversarial examples by adding small …

Without access to the training data where a black-box victim model is deployed, training a
surrogate model for black-box adversarial attack is still a struggle. In terms of data, we …

Most existing deep neural networks are susceptible to the influence of adversarial examples,
which may cause them to output incorrect prediction results. An adversarial example is the …

Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples
resulting from adding small-magnitude perturbations to inputs. Such adversarial examples …

Neural networks are vulnerable to adversarial examples, malicious inputs crafted to fool
trained models. Adversarial examples often exhibit black-box transfer, meaning that …

Though CNNs have achieved the state-of-the-art performance on various vision tasks, they
are vulnerable to adversarial examples---crafted by adding human-imperceptible …

Existing black-box attacks on deep neural networks (DNNs) so far have largely focused on
transferability, where an adversarial instance generated for a locally trained model can" …

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 …

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