Search Results for author:
Found 2 papers, 0 papers with code
The Earth is Flat? Unveiling Factual Errors in Large Language Models
Current methods for evaluating LLMs' veracity are limited by test data leakage or the need for extensive human labor, hindering efficient and accurate error detection.
LFAA: Crafting Transferable Targeted Adversarial Examples with Low-Frequency Perturbations
In this work, we present a novel approach to generate transferable targeted adversarial examples by exploiting the vulnerability of deep neural networks to perturbations on high-frequency components of images.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
