The rapid development of artificial intelligence (AI) and breakthroughs in Internet of Things
(IoT) technologies have driven the innovation of advanced autonomous driving systems …

Transformer emerges as a powerful tool for visual recognition. In addition to demonstrating
competitive performance on a broad range of visual benchmarks, recent works also argue …

Adversarial training suffers from robust overfitting, a phenomenon where the robust test
accuracy starts to decrease during training. In this paper, we focus on reducing robust …

As a research community, we are still lacking a systematic understanding of the progress on
adversarial robustness which often makes it hard to identify the most promising ideas in …

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 …

The field of defense strategies against adversarial attacks has significantly grown over the
last years, but progress is hampered as the evaluation of adversarial defenses is often …

The study on improving the robustness of deep neural networks against adversarial
examples grows rapidly in recent years. Among them, adversarial training is the most …

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 Learning is currently used to perform multiple tasks, such as object recognition, face
recognition, and natural language processing. However, Deep Neural Networks (DNNs) are …

In the past few decades, artificial intelligence (AI) technology has experienced swift
developments, changing everyone's daily life and profoundly altering the course of human …