Nature

Dual-targeted adversarial noise for 3D point cloud classification model

In this paper, we introduce a novel method for generating dual-target adversarial examples in point cloud data, specifically designed to cause different models to misclassify into distinct ...
Nature

Efficient black-box attack with surrogate models and multiple universal adversarial perturbations

Deep learning models are inherently vulnerable to adversarial examples, particularly in black-box settings where attackers have limited knowledge of the target model. Existing attack algorithms often ...
EurekAlert!

A prompt-based approach to adversarial example generation and robustness enhancement

Recent years have seen the wide application of NLP models in crucial areas such as finance, medical treatment, and news media, raising concerns about the model robustness. Existing methods are mainly ...
Phys.org

Efficient adversarial robustness evaluation of AI models with limited access

Recent studies have identified the lack of robustness in current AI models against adversarial examples—intentionally manipulated prediction-evasive data inputs that are similar to normal data but ...
VentureBeat

Adversarial attacks in machine learning: What they are and how to stop them

Adversarial machine learning, a technique that attempts to fool models with deceptive data, is a growing threat in the AI and machine learning research community. The most common reason is to cause a ...
Forbes

Adversarial Attacks On AI Systems

We are witnessing a rapid advancement of AI and its impact across various industries. However, with great power comes great responsibility, and one of the emerging challenges in the AI landscape is ...