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?

翻译：将人类分析纳入深度神经网络有望提升其鲁棒性，这一方向在对抗性机器学习文献中尚属相对未探明的领域。研究表明，神经网络可视化解释图容易遭受对抗攻击。为使图像分析师能评估给定模型的可解释性，需要进一步研究以选择鲁棒的可视化解释方案。这些因素严重影响了基于对抗样本的"人在回路"（HITL）评估工具——包括解释图与鲁棒性度量。我们认为，人类视觉注意力模型有望提升人机图像分析系统的可解释性与鲁棒性。当前面临的挑战是：在这类对抗环境下，如何使HITL评估具备鲁棒性？