这篇论文教你如何设计测试策略，即使有人用 AI 作弊也能得到真实结果。
该研究提出最优测试策略，即使存在作弊者污染结果，仍能恢复所需测试结果。研究使用动态规划方法确定最优测试策略，通过不同安全措施对选定测试组进行重新测试。随着 AI 技术发展，使用 AI 技术作弊导致的测试扭曲正变得更加普遍和严重。
Optimal Adversarial Testing: Extracting Honest Test Results from Dishonest Test Takers
In applications, it is often required to test objects or people to determine their qualities in terms of certain metrics. However, besides being naturally noisy, the test results can be corrupted by adversarial behaviors of objects or people being tested (test takers). For example, dishonest test takers can cheat in the exams to distort the test results. With the development of AI technologies, such distortions driven by cheating using AI technologies are becoming more commonplace and severe. In this paper, we propose optimal testing strategies which can still recover needed test results even if there are cheaters polluting the results. The proposed testing strategies will optimally re-test selected group of test takers using different testing security measures. We determine the optimal testing strategies using a dynamic programming method.