Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

The outstanding performance of deep neural networks has promoted deep learning
applications in a broad set of domains. However, the potential risks caused by 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 …

Chain-of-thought (CoT) advances the reasoning abilities of large language models (LLMs)
and achieves superior performance in complex reasoning tasks. However, most CoT studies …

The goal of a decision-based adversarial attack on a trained model is to generate
adversarial examples based solely on observing output labels returned by the targeted …

We propose an intriguingly simple method for the construction of adversarial images in the
black-box setting. In constrast to the white-box scenario, constructing black-box adversarial …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

We consider the black-box adversarial setting, where the adversary has to generate
adversarial perturbations without access to the target models to compute gradients. Previous …

Simulators offer the possibility of safe, low-cost development of self-driving systems.
However, current driving simulators exhibit naïve behavior models for background traffic …

With wide deployment of machine learning (ML)-based systems for a variety of applications
including medical, military, automotive, genomic, multimedia, and social networking, there is …