Adversarial Robustness in Machine Learning

The paper demonstrates how adversarial attacks can corrupt genomic analyses and explores machine learning techniques to detect such manipulations.

This study shows that robust deep learning models for object recognition rely on low spatial frequency information, as evidenced by their performance on adversarial attacks and common corruptions.

This paper introduces a framework for generating adversarial opponents that influence human decision-making in specific tasks, demonstrating its effectiveness through experiments on action selection and response inhibition.