SOURCE-LINKED INTELLIGENCE
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
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-28T14:13:40.000Z
First collected: 2026-09-21T08:02:06.831Z. This is not the publication date.