Malware poses a severe threat to cyber security. Attackers use malware to achieve their
malicious purposes, such as unauthorized access, stealing confidential data, blackmailing …

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 …

With the significant development of large models in recent years, Large Vision-Language
Models (LVLMs) have demonstrated remarkable capabilities across a wide range of …

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 …

Achieving robustness to distributional shift is a longstanding and challenging goal of
computer vision. Data augmentation is a commonly used approach for improving …

Autonomous driving systems have witnessed significant development during the past years
thanks to the advance in machine learning-enabled sensing and decision-making …

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 …

Image classification models can depend on multiple different semantic attributes of the
image. An explanation of the decision of the classifier needs to both discover and visualize …

In recent years, as various realistic face forgery techniques known as DeepFake improves
by leaps and bounds, more and more DeepFake detection techniques have been proposed …

Deep neural networks have achieved unprecedented success on diverse vision tasks.
However, they are vulnerable to adversarial noise that is imperceptible to humans. This …