Overcoming Adversarial Attacks for Human-in-the-Loop Applications

Autor:	McCoppin, Ryan, Kennedy, Marla, Lukyanenko, Platon, Kennedy, Sean
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Including human analysis has the potential to positively affect the robustness of Deep Neural Networks and is relatively unexplored in the Adversarial Machine Learning literature. Neural network visual explanation maps have been shown to be prone to adversarial attacks. Further research is needed in order to select robust visualizations of explanations for the image analyst to evaluate a given model. These factors greatly impact Human-In-The-Loop (HITL) evaluation tools due to their reliance on adversarial images, including explanation maps and measurements of robustness. We believe models of human visual attention may improve interpretability and robustness of human-machine imagery analysis systems. Our challenge remains, how can HITL evaluation be robust in this adversarial landscape? Comment: New Frontiers in Adversarial Machine Learning, ICML 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.05952 Zobrazit plný text záznamu View this record from Arxiv