Overcoming Adversarial Attacks for Human-in-the-Loop Applications
This tackles the problem of making human-in-the-loop evaluation tools more robust against adversarial attacks for image analysts, but it appears incremental as it builds on existing adversarial machine learning research.
The paper addresses the vulnerability of neural network visual explanation maps to adversarial attacks in Human-in-the-Loop applications, proposing that models of human visual attention could enhance interpretability and robustness, but does not present specific results or numbers.
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?