Adversarial Robustness in Machine Learning

Adversarial Attacks on Genotype Sequences

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

Robust deep learning object recognition models rely on low frequency information in natural images

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.

Adversarial manipulation of human decision-making

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.