Revealing Perceptual Proxies with Adversarial Examples
Revealing Perceptual Proxies with Adversarial Examples
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DOI：
10.1109/tvcg.2020.3030429
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发表时间：
2020-07
影响因子：
5.2
通讯作者：
Brian D. Ondov;Fumeng Yang;M. Kay;N. Elmqvist;S. Franconeri
中科院分区：
文献类型：
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作者：
Brian D. Ondov;Fumeng Yang;M. Kay;N. Elmqvist;S. Franconeri
数据可视化将数字转换为视觉标记，这样我们的视觉系统就可以从图像中提取数据，而不是原始数字。显然，视觉系统并不像计算机那样计算这些值，而是算术平均值或相关性。相反，它使用感知代理提取这些模式;视觉标记的启发式快捷方式，例如质心或形状包络。了解人们使用哪些代理将导致更有效的可视化。我们提出了一系列众包实验的结果，这些实验测量了一组候选代理在比较以条形图形式呈现的成对数据系列的平均值和范围时能够解释人类表现的能力。我们生成的数据集中，正确的答案-具有较大算术平均值或范围的系列-与“对抗性”系列进行比较，如果观众使用特定的候选代理，则应该被视为较大。我们使用贝叶斯逻辑回归模型和一个稳健的贝叶斯混合效应线性模型来衡量每个对抗性代理会促使观众做出错误回答的程度，以及不同的人是否会使用不同的代理。最后，我们尝试从头开始构建对抗性数据集，使用迭代众包程序来执行黑盒优化。
Data visualizations convert numbers into visual marks so that our visual system can extract data from an image instead of raw numbers. Clearly, the visual system does not compute these values as a computer would, as an arithmetic mean or a correlation. Instead, it extracts these patterns using perceptual proxies; heuristic shortcuts of the visual marks, such as a center of mass or a shape envelope. Understanding which proxies people use would lead to more effective visualizations. We present the results of a series of crowdsourced experiments that measure how powerfully a set of candidate proxies can explain human performance when comparing the mean and range of pairs of data series presented as bar charts. We generated datasets where the correct answer-the series with the larger arithmetic mean or range-was pitted against an “adversarial” series that should be seen as larger if the viewer uses a particular candidate proxy. We used both Bayesian logistic regression models and a robust Bayesian mixed-effects linear model to measure how strongly each adversarial proxy could drive viewers to answer incorrectly and whether different individuals may use different proxies. Finally, we attempt to construct adversarial datasets from scratch, using an iterative crowdsourcing procedure to perform black-box optimization.