Remote sensing image analysis technology based on neural networks has significantly
facilitated human life. However, adversarial attacks can drastically impair the performance of …

Deep neural networks (DNNs) have been shown to be vulnerable against adversarial
examples (AEs), which are maliciously designed to cause dramatic model output errors. In …

This comprehensive research presents and investigates a diverse assessment of
interruption discovery strategies and their job in contemporary online protection. Interruption …

Evading adversarial example detection defenses requires finding adversarial examples that
must simultaneously (a) be misclassified by the model and (b) be detected as non …

Machine Learning (ML) techniques can facilitate the automation of mal icious soft ware
(malware for short) detection, but suffer from evasion attacks. Many studies counter such …

Correctly classifying adversarial examples is an essential but challenging requirement for
safely deploying machine learning models. As reported in RobustBench, even the state-of …

While deep neural networks (DNNs) achieve impressive performance on environment
perception tasks, their sensitivity to adversarial perturbations limits their use in practical …

The decision of neural networks is easily shifted at an attacker's will by so-called adversarial
attacks. Initially only successful when directly applied to the input, recent advances allow …

Deep neural network (DNN) accelerators received considerable attention in recent years
due to the potential to save energy compared to mainstream hardware. Low-voltage …

Although deep neural networks (DNNs) have performed well on many perceptual tasks, they
are vulnerable to adversarial examples that are generated by adding slight but maliciously …