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 networks have achieved unprecedented success on diverse vision tasks.
However, they are vulnerable to adversarial noise that is imperceptible to humans. This …

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 …

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. It is thus
imperative to devise effective attack algorithms to identify the deficiencies of DNNs …

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 …

Transferable adversarial attacks against Deep neural networks (DNNs) have received broad
attention in recent years. An adversarial example can be crafted by a surrogate model and …

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 …

Batch normalization (BN) has been widely used in modern deep neural networks (DNNs)
due to improved convergence. BN is observed to increase the model accuracy while at the …

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