Department of Psychological & Brain Sciences, Johns Hopkins University, Baltimore, MD, USA.
Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA.
J Vis. 2023 Apr 3;23(4):4. doi: 10.1167/jov.23.4.4.
Machine recognition systems now rival humans in their ability to classify natural images. However, their success is accompanied by a striking failure: a tendency to commit bizarre misclassifications on inputs specifically selected to fool them. What do ordinary people know about the nature and prevalence of such classification errors? Here, five experiments exploit the recent discovery of "natural adversarial examples" to ask whether naive observers can predict when and how machines will misclassify natural images. Whereas classical adversarial examples are inputs that have been minimally perturbed to induce misclassifications, natural adversarial examples are simply unmodified natural photographs that consistently fool a wide variety of machine recognition systems. For example, a bird casting a shadow might be misclassified as a sundial, or a beach umbrella made of straw might be misclassified as a broom. In Experiment 1, subjects accurately predicted which natural images machines would misclassify and which they would not. Experiments 2 through 4 extended this ability to how the images would be misclassified, showing that anticipating machine misclassifications goes beyond merely identifying an image as nonprototypical. Finally, Experiment 5 replicated these findings under more ecologically valid conditions, demonstrating that subjects can anticipate misclassifications not only under two-alternative forced-choice conditions (as in Experiments 1-4), but also when the images appear one at a time in a continuous stream-a skill that may be of value to human-machine teams. We suggest that ordinary people can intuit how easy or hard a natural image is to classify, and we discuss the implications of these results for practical and theoretical issues at the interface of biological and artificial vision.
机器识别系统在分类自然图像的能力上现在已经可以与人类相媲美。然而，它们的成功伴随着一个惊人的失败：它们有一种倾向，会对专门设计来欺骗它们的输入产生离奇的错误分类。那么普通人对这种分类错误的性质和普遍性了解多少呢？在这里，五个实验利用最近发现的“自然对抗样本”来探讨，天真的观察者是否能够预测机器何时以及如何错误地分类自然图像。虽然经典的对抗样本是经过最小程度的扰动以诱导错误分类的输入，但自然对抗样本只是未经修改的自然照片，它们会一直欺骗各种机器识别系统。例如，一只投下阴影的鸟可能被错误分类为日晷，或者一把用稻草制成的沙滩伞可能被错误分类为扫帚。在实验 1 中，被试准确地预测了机器将错误分类哪些自然图像，以及哪些不会。实验 2 到 4 将这种能力扩展到了图像将如何被错误分类，表明预测机器错误分类不仅仅是识别一个图像是否非典型。最后，实验 5 在更符合生态条件下复制了这些发现，证明被试不仅可以在二选一的强制选择条件下（如实验 1-4）预测错误分类，还可以在图像一次一个地出现在连续流中预测错误分类——这一技能可能对人机团队有价值。我们认为，普通人可以凭直觉判断一个自然图像是容易还是难以分类，我们还讨论了这些结果对生物和人工视觉界面的实际和理论问题的影响。
