论文标题
现实世界中的道德测试：评估对抗机器学习的物理测试
Ethical Testing in the Real World: Evaluating Physical Testing of Adversarial Machine Learning
论文作者
论文摘要
本文批判性地评估了各种对抗机器学习（ML）对涉及人类受试者的计算机视觉系统的攻击的物理领域测试的充分性和代表性。许多部署此类攻击的论文将自己描述为“现实世界”。但是，尽管进行了这种框架，但我们发现进行的物理或现实世界测试很少，几乎没有提供有关测试对象的细节，并且经常以事后的想法或示范进行。在没有代表性试验或测试的情况下，对抗性的ML研究是一种道德，科学和健康/安全问题，可能造成真正的危害。我们介绍了问题和方法，然后批评了该文章中论文所采用的物理领域测试方法。然后，我们探索了对抗性ML中更具包容性物理测试的各种障碍，并提供建议以改善这些挑战。
This paper critically assesses the adequacy and representativeness of physical domain testing for various adversarial machine learning (ML) attacks against computer vision systems involving human subjects. Many papers that deploy such attacks characterize themselves as "real world." Despite this framing, however, we found the physical or real-world testing conducted was minimal, provided few details about testing subjects and was often conducted as an afterthought or demonstration. Adversarial ML research without representative trials or testing is an ethical, scientific, and health/safety issue that can cause real harms. We introduce the problem and our methodology, and then critique the physical domain testing methodologies employed by papers in the field. We then explore various barriers to more inclusive physical testing in adversarial ML and offer recommendations to improve such testing notwithstanding these challenges.
