Đánh giá độ tin cậy của phần mềm phát hiện AI

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Alshahrani, A. (2024). Artificial intelligence technologies utilization for detecting explosive materials. International Journal of Data and Network Science, 8(1), 617-628.

Bellini, V., Semeraro, F., Montomoli, J., Cascella, M., & Bignami, E. (2024). Between human and AI: Assessing the reliability of AI text detection tools. Current Medical Research and Opinion, 40(3), 353-358. https://doi.org/10.1080/03007995.2024.2310086

Chakraborty, S., Bedi, A. S., Zhu, S., An, B., Manocha, D., & Huang, F. (2023). On the possibilities of AI-generated text detection. https://doi.org/10.48550/arXiv.2304.04736

Cui, W., Zhang, L., Wang, Q., & Cai, S. (2023). Who said that? Benchmarking social media AI detection. http://arxiv.org/abs/2310.08240

Hu, X., Chen, P. Y., & Ho, T. Y. (2023). RADAR: Robust AI-text detection via adversarial learning. https://doi.org/10.48550/arXiv.2307.03838

Krishna, K., Song, Y., Karpinska, M., Wieting, J., & Iyyer, M. (2023). Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense. https://doi.org/10.48550/arXiv.2303.13408

Kumarage, T., Sheth, P., Moraffah, R., Garland, J., & Liu, H. (2023). How reliable are AI-generated-text detectors? An assessment framework using evasive soft prompts. https://doi.org/10.48550/arXiv.2310.05095

Legaspi, J. B., Licuben, R. J. O., Legaspi, E. A., & Tolentino, J. A. (2024). Comparing AI detectors: Evaluating performance and efficiency. International journal of Science and Research Archive, 12(2), 833-838.

Lu, N., Liu, S., He, R., & Tang, K. (2023). Large language models can be guided to evade AI-generated text detection. https://doi.org/10.48550/arXiv.2305.10847

Miller, I., & Lin, D. (2022). Developing self-evolving deepfake detectors against AI attacks [Paper presentation]. 2022 IEEE 4th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS-ISA), Atlanta, GA. https://ieeexplore.ieee.org/abstract/document/10063474/

Pandita, V., Mujawar, A. M., Norbu, T., Verma, V., & Patil, P. (2024). Text origin detection: Unmasking the source - AI vs human [Paper presentation]. 2024 5th International Conference for Emerging Technology (INCET), Belgaum, India. https://ieeexplore.ieee.org/abstract/document/10592990/

Rashidi, H. H., Fennell, B. D., Albahra, S., Hu, B., & Gorbett, T. (2023). The ChatGPT conundrum: Human-generated scientific manuscripts misidentified as AI creations by AI text detection tool. Journal of Pathology Informatics, 14, Article 100342.

Sadasivan, V. S., Kumar, A., Balasubramanian, S., Wang, W., & Feizi, S. (2023). Can AI-generated text be reliably detected? https://doi.org/10.48550/arXiv.2303.11156

Verma, V., Fleisig, E., Tomlin, N., & Klein, D. (2023). Ghostbuster: Detecting text ghostwritten by large language models. https://doi.org/10.48550/arXiv.2305.15047

Walters, W. H. (2023). The effectiveness of software designed to detect AI-generated writing:
A comparison of 16 AI text detectors. Open Information Science, 7. https://doi.org/10.1515/opis-2022-0158

Wang, Y. (2024). Survey for detecting AI-generated content. Advances in Engineering Technology Research, 11(1), 643-643.

Weber-Wulff, D., Anohina-Naumeca, A., Bjelobaba, S., Foltýnek, T., Guerrero-Dib, J., Popoola, O., Šigut, P., & Waddington, L. (2023). Testing of detection tools for AI-generated text. International Journal for Educational Integrity, 19(1), Article 26. https://doi.org/10.1007/s40979-023-00146-z

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