Deep Learning algorithms have achieved state-of-the-art performance for Image
Classification. For this reason, they have been used even in security-critical applications …

Abstract Purpose of Review Artificial intelligence (AI) is often presented as a transformative
technology for clinical medicine even though the current technology maturity of AI is low. The …

Abstract We propose the Square Attack, a score-based black-box l_2 l 2-and l_ ∞ l∞-
adversarial attack that does not rely on local gradient information and thus is not affected by …

Data Poisoning attacks modify training data to maliciously control a model trained on such
data. In this work, we focus on targeted poisoning attacks which cause a reclassification of …

Though deep neural networks (DNNs) have demonstrated excellent performance in
computer vision, they are susceptible and vulnerable to carefully crafted adversarial …

SentiNet is a novel detection framework for localized universal attacks on neural networks.
These attacks restrict adversarial noise to contiguous portions of an image and are reusable …

Despite excellent performance on stationary test sets, deep neural networks (DNNs) can fail
to generalize to out-of-distribution (OoD) inputs, including natural, non-adversarial ones …

Deep neural networks (DNNs) are vulnerable to adversarial examples where inputs with
imperceptible perturbations mislead DNNs to incorrect results. Recently, adversarial patch …

Patch attacks, one of the most threatening forms of physical attack in adversarial examples,
can lead networks to induce misclassification by modifying pixels arbitrarily in a continuous …

Does the human mind resemble the machines that can behave like it? Biologically inspired
machine-learning systems approach “human-level” accuracy in an astounding variety of …