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Optimal Adversarial Testing: Extracting Honest Test Results from Dishonest Test Takers — arXiv:2608.28362

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.28362
License (as archived): CC0-1.0
Archived: 2026-10-17T10:48:16.596Z
SHA-256 of the archived text: 4442ac070386060d589742dade25f488bc24642b6ecd0443a55d6d39d8452781
Stable URL of this page: https://metraipolis.com/documento/dc_5e4c3f684dfd

How to cite

Optimal Adversarial Testing: Extracting Honest Test Results from Dishonest Test Takers — arXiv:2608.28362 — arXiv (en), https://arxiv.org/abs/2608.28362 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_5e4c3f684dfd (Metraipolis archive dc_5e4c3f684dfd, sha256 4442ac070386060d589742dade25f488bc24642b6ecd0443a55d6d39d8452781)

BibTeX

@misc{dc5e4c3f684dfd,
  title = {Optimal Adversarial Testing: Extracting Honest Test Results from Dishonest Test Takers — arXiv:2608.28362},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.28362},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_5e4c3f684dfd; sha256 4442ac070386060d589742dade25f488bc24642b6ecd0443a55d6d39d8452781},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.28362. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-31 in cs.AI).
Categorie arXiv: Cryptography and Security (cs.CR) ; Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Signal Processing (eess.SP); Applications (stat.AP); Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2608.28362 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T10:33:42.524Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-31?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).