Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption,
with machine learning systems demonstrating superhuman performance in a significant …

The newly emerged machine learning (eg, deep learning) methods have become a strong
driving force to revolutionize a wide range of industries, such as smart healthcare, financial …

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

Adversarial training (AT) is always formulated as a minimax problem, of which the
performance depends on the inner optimization that involves the generation of adversarial …

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 …

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) …

Given the severe vulnerability of Deep Neural Networks (DNNs) against adversarial
examples, there is an urgent need for an effective adversarial attack to identify the …

Deep neural networks are vulnerable to adversarial examples, which becomes one of the
most important research problems in the development of deep learning. While a lot of efforts …

Deep learning technology is increasingly being applied in safety-critical scenarios but has
recently been found to be susceptible to imperceptible adversarial perturbations. This raises …

Although deep neural networks (DNNs) have made rapid progress in recent years, they are
vulnerable in adversarial environments. A malicious backdoor could be embedded in a …