… adversarial attacks on deep neural networks achieving k-misclassification for k > 1. We also
provide adversarial attacks … sidered in the context of deep neural networks by Papernot et al. […

… the network. In fact, some of the latest findings suggest that the existence of adversarial attacks
… To address this problem, we study the adversarial robustness of neural networks through …

… Such adversarial examples have been extensively studied in the context of computer vision
… In this work, we show adversarial attacks are also effective when targeting neural network …

… Various attempts have been conducted to defend adversarial attacks. Papernot et al. [25] …
was still highly vulnerable to attacks [3]. Recently, the adversarial training strategy became …

… deep neural networks (DNNs) can be easily fooled by adversarial … in attacking and defending
DNNs with adversarial examples … is to review the types of adversarial attacks and defenses, …

… In our analysis to defend neural networks against adversarial attacks, we extend on the
work in [3] which suggests a novel approach for fast generation of adversarial examples. This …

… • Model: We propose a model for adversarial attacks on attributed graphs considering … of
attacks where we explicitly distinguish between the attacker and the target nodes. Our attacks …

… about their robustness to adversarial attacks. Yet, in … adversarial attacks on attributed graphs,
specifically focusing on models exploiting ideas of graph convolutions. In addition to attacks …

… of adversarial attacks for 19 different neural networks trained to be robust against adversarial
attacks on the … of adversarial attacks. We benchmark Jitter against 5 state-of-the-art (SOTA) …

… We compare our model to baselines under three kinds of adversarial attacks: direct targeted
attack (Nettack-Di [8]), influence targeted attack (NettackIn [8]), and non-targeted attack (…