Adversarial machine learning (AML) studies attacks that can fool machine
learning algorithms into generating incorrect outcomes as well as the defenses
against worst-case attacks to strengthen model robustness. Specifically for
image classification, it is challenging to understand adversarial attacks due
to their use of subtle perturbations that are not human-interpretable, as well
as the variability of attack impacts influenced by diverse methodologies,
instance differences, and model architectures. Through a design study with AML
learners and teachers, we introduce AdvEx, a multi-level interactive
visualization system that comprehensively presents the properties and impacts
of evasion attacks on different image classifiers for novice AML learners. We
quantitatively and qualitatively assessed AdvEx in a two-part evaluation
including user studies and expert interviews. Our results show that AdvEx is
not only highly effective as a visualization tool for understanding AML
mechanisms, but also provides an engaging and enjoyable learning experience,
thus demonstrating its overall benefits for AML learners.

通过与 AML 学习者和教师的设计研究，引入 AdvEx 作为一个多层次交互式可视化系统，全面呈现对新手 AML 学习者的不同图像分类器的逃避攻击的性质和影响，我们定量和定性评估了 AdvEx，并通过用户研究和专家访谈的两部分评估结果表明，AdvEx 不仅作为理解 AML 机制的可视化工具非常有效，而且提供了一种有吸引力和愉快的学习体验，从而显示其对 AML 学习者的整体益处。

熊猫还是不是熊猫？通过交互式可视化理解对抗攻击

Panda or not Panda? Understanding Adversarial Attacks with Interactive  Visualization

While deep learning models have achieved state-of-the-art accuracies for many
prediction tasks, understanding these models remains a challenge. Despite the
recent interest in developing visual tools to help users interpret deep
learning models, the complexity and wide variety of models deployed in
industry, and the large-scale datasets that they used, pose unique design
challenges that are inadequately addressed by existing work. Through
participatory design sessions with over 15 researchers and engineers at
Facebook, we have developed, deployed, and iteratively improved ActiVis, an
interactive visualization system for interpreting large-scale deep learning
models and results. By tightly integrating multiple coordinated views, such as
a computation graph overview of the model architecture, and a neuron activation
view for pattern discovery and comparison, users can explore complex deep
neural network models at both the instance- and subset-level. ActiVis has been
deployed on Facebook's machine learning platform. We present case studies with
Facebook researchers and engineers, and usage scenarios of how ActiVis may work
with different models.

本文介绍了一种名为 ActiVis 的交互式可视化系统，可用于解释深度学习模型的大规模数据集，并通过 Facebook 的机器学习平台进行了部署和迭代改进。