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題名 在自然語言處理中同時抵禦內部雜訊與外部攻擊的雙向防禦框架
A Dual Defense Framework Against Internal Noise and External Attacks in Natural Language Processing
作者 莊格維
Chuang, Ko-Wei
貢獻者 李蔡彥<br>黃瀚萱
Tsai-Yen Li<br>Hen-Hsen Huang
莊格維
Chuang, Ko-Wei
關鍵詞 雜訊標籤學習
群眾學習
對抗意識攻擊
對抗訓練
Noisy label learning
Learning from Crowdsourcing
Adversarial-awareness-attack
Adversarial training
日期 2024
上傳時間 4-Sep-2024 14:35:42 (UTC+8)
摘要 近年來，隨著大型語言模型（如Chat GPT）和通用人工智慧（AGI）及生成式人工智慧（GAI）模型的出現，這些技術引起了廣泛關注和研究。與此同時，人們對於人工智慧可能取代人類工作的擔憂日益增加，這種擔憂導致許多從業者試圖故意減緩人工智慧的發展，進而對人工智慧的進步構成挑戰。 在實際職場調查中，我們觀察到兩個值得關注的現象：第一，外部競爭者或廣告業者試圖利用模型的辨識弱點，對模型進行對抗性攻擊；第二，內部從業人員故意上傳錯誤的訓練資料，導致語言模型的準確率下降。 本研究旨在探討如何同時防範來自內部和外部的威脅，即對抗性標記者提供的錯誤標籤和外部使用者進行的對抗性攻擊，以確保模型的穩健性，並提升語言模型在雙重攻擊下的強韌性。 透過實驗和現實情境資料集的收集與分析，我們驗證了一系列演算法在抵抗對抗者上傳錯誤資料和辨識對抗攻擊方面的有效性。除此之外，我們的方法將基於圖像分類任務的先進雜訊標籤學習方法，替換成應用於基於自然語言處理方法，並證實了這些雜訊標籤學習方法在樣本特徵為圖像陣列以及文字向量時皆可以有效的實現，從而進一步證明了我們框架的價值和可靠性。 儘管本研究仍存在一些需要克服的限制，但我們相信，研究結果為未來可能面臨更多人為干擾的情境提供了更加穩健的防禦策略和構想。
In recent years, with the emergence of large language models such as Chat GPT, Artificial General Intelligence (AGI), and Generative Artificial Intelligence (GAI) models, these technologies have garnered widespread attention and research. Concurrently, concerns about AI potentially replacing human jobs have increased, leading many professionals to deliberately slow down AI development, posing challenges to its advancement. In our workplace investigation, we observed two noteworthy phenomena: first, external competitors or advertisers attempting to exploit model recognition weaknesses to conduct adversarial attacks; second, internal personnel intentionally uploading erroneous training data, causing a decline in language model accuracy. This study aims to explore how to simultaneously defend against threats from both internal and external sources, namely, adversarial annotators providing incorrect labels and external users conducting adversarial attacks, to ensure model robustness and enhance the resilience of language models under dual attacks. Through experiments and analysis of real-world datasets, we validated a series of algorithms that effectively counteract erroneous data uploads by adversaries and recognize adversarial attacks. Additionally, our approach adapts advanced noisy label learning methods, originally developed for image classification tasks, to natural language processing (NLP) methods, demonstrating that these noisy label learning methods are effective for both image arrays and text vectors. This further underscores the value and reliability of our framework. Despite some limitations that still need to be addressed, we believe our research results provide more robust defense strategies and insights for future scenarios potentially facing more human-induced interference.
參考文獻 [1] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” May 09, 2016, arXiv: arXiv:1506.02640. doi: 10.48550/arXiv.1506.02640. [2] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems, Curran Associates, Inc., 2012. Accessed: Mar. 03, 2024. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html [3] K. A. Hambarde and H. Proenca, “Information Retrieval: Recent Advances and Beyond,” IEEE Access, vol. 11, pp. 76581–76604, 2023, doi: 10.1109/ACCESS.2023.3295776. [4] W. Zhang, T. Du, and J. Wang, “Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction,” Jan. 11, 2016, arXiv: arXiv:1601.02376. doi: 10.48550/arXiv.1601.02376. [5] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” May 24, 2019, arXiv: arXiv:1810.04805. Accessed: Mar. 04, 2024. [Online]. Available: http://arxiv.org/abs/1810.04805 [6] A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever, “Improving Language Understanding by Generative Pre-Training”. [7] J. Howard and S. Ruder, “Universal Language Model Fine-tuning for Text Classification,” May 23, 2018, arXiv: arXiv:1801.06146. doi: 10.48550/arXiv.1801.06146. [8] G. Damioli, V. Van Roy, and D. Vertesy, “The impact of artificial intelligence on labor productivity,” Eurasian Bus. Rev., vol. 11, no. 1, pp. 1–25, Mar. 2021, doi: 10.1007/s40821-020-00172-8. [9] M.-H. Huang and R. T. Rust, “Artificial Intelligence in Service,” J. Serv. Res., vol. 21, no. 2, pp. 155–172, May 2018, doi: 10.1177/1094670517752459. [10] D. Angluin and P. Laird, “Learning from noisy examples,” Mach. Learn., vol. 2, no. 4, pp. 343–370, Apr. 1988, doi: 10.1007/BF00116829. [11] B. Frenay and M. Verleysen, “Classification in the Presence of Label Noise: A Survey,” IEEE Trans. Neural Netw. Learn. Syst., vol. 25, no. 5, pp. 845–869, May 2014, doi: 10.1109/TNNLS.2013.2292894. [12] B. Han et al., “Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels,” Oct. 30, 2018, arXiv: arXiv:1804.06872. doi: 10.48550/arXiv.1804.06872. [13] L. P. F. Garcia, J. Lehmann, A. C. P. L. F. de Carvalho, and A. C. Lorena, “New label noise injection methods for the evaluation of noise filters,” Knowl.-Based Syst., vol. 163, pp. 693–704, Jan. 2019, doi: 10.1016/j.knosys.2018.09.031. [14] B. Han et al., “A Survey of Label-noise Representation Learning: Past, Present and Future,” Feb. 20, 2021, arXiv: arXiv:2011.04406. doi: 10.48550/arXiv.2011.04406. [15] D. Zhu, M. A. Hedderich, F. Zhai, D. I. Adelani, and D. Klakow, “Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text Classification,” Apr. 20, 2022, arXiv: arXiv:2204.09371. Accessed: May 24, 2024. [Online]. Available: http://arxiv.org/abs/2204.09371 [16] G. Dawson and R. Polikar, “Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awareness,” Jun. 04, 2021, arXiv: arXiv:2105.14083. Accessed: Mar. 01, 2024. [Online]. Available: http://arxiv.org/abs/2105.14083 [17] G. Hermosillovaladez, C. Florin, L. Bogoni, L. Moy, Raykar, Vikas, and Shipeng, Yu, “Learning From Crowds”. [18] H. Song, M. Kim, D. Park, Y. Shin, and J.-G. Lee, “Learning from Noisy Labels with Deep Neural Networks: A Survey,” Mar. 09, 2022, arXiv: arXiv:2007.08199. Accessed: Mar. 04, 2024. [Online]. Available: http://arxiv.org/abs/2007.08199 [19] I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and Harnessing Adversarial Examples,” Mar. 20, 2015, arXiv: arXiv:1412.6572. doi: 10.48550/arXiv.1412.6572. [20] I. J. Goodfellow et al., “Generative Adversarial Networks,” Jun. 10, 2014, arXiv: arXiv:1406.2661. doi: 10.48550/arXiv.1406.2661. [21] Y. Zhang, S. Zheng, P. Wu, M. Goswami, and C. Chen, “LEARNING WITH FEATURE-DEPENDENT LABEL NOISE: A PROGRESSIVE APPROACH,” 2021. [22] J. Li, R. Socher, and S. C. H. Hoi, “DivideMix: Learning with Noisy Labels as Semi-supervised Learning,” Feb. 18, 2020, arXiv: arXiv:2002.07394. Accessed: Mar. 01, 2024. [Online]. Available: http://arxiv.org/abs/2002.07394 [23] P. Chen, J. Ye, G. Chen, J. Zhao, and P.-A. Heng, “Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise,” Dec. 10, 2020, arXiv: arXiv:2012.05458. Accessed: Mar. 04, 2024. [Online]. Available: http://arxiv.org/abs/2012.05458 [24] A. Kurakin, I. Goodfellow, and S. Bengio, “Adversarial Machine Learning at Scale,” Feb. 10, 2017, arXiv: arXiv:1611.01236. Accessed: Mar. 03, 2024. [Online]. Available: http://arxiv.org/abs/1611.01236 [25] T. Bai, J. Luo, J. Zhao, B. Wen, and Q. Wang, “Recent Advances in Adversarial Training for Adversarial Robustness,” Apr. 20, 2021, arXiv: arXiv:2102.01356. doi: 10.48550/arXiv.2102.01356. [26] P. L. Bartlett, M. I. Jordan, and J. D. McAuliffe, “Convexity, Classification, and Risk Bounds.” Accessed: Mar. 07, 2024. [Online]. Available: https://www.tandfonline.com/doi/abs/10.1198/016214505000000907 [27] N. D. Lawrence, B. Scholkopf, and T. Net, “Estimating a Kernel Fisher Discriminant in the Presence of Label Noise”. [28] T. Liu and D. Tao, “Classification with Noisy Labels by Importance Reweighting,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 3, pp. 447–461, Mar. 2016, doi: 10.1109/TPAMI.2015.2456899. [29] C. Dong, L. Liu, and J. Shang, “Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting,” Oct. 12, 2023, arXiv: arXiv:2110.03135. Accessed: Mar. 04, 2024. [Online]. Available: http://arxiv.org/abs/2110.03135 [30] W. Simoncini and G. Spanakis, “SeqAttack: On Adversarial Attacks for Named Entity Recognition,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, H. Adel and S. Shi, Eds., Online and Punta Cana, Dominican Republic: Association for Computational Linguistics, Nov. 2021, pp. 308–318. doi: 10.18653/v1/2021.emnlp-demo.35. [31] A. Farahani, S. Voghoei, K. Rasheed, and H. R. Arabnia, “A Brief Review of Domain Adaptation,” in Advances in Data Science and Information Engineering, R. Stahlbock, G. M. Weiss, M. Abou-Nasr, C.-Y. Yang, H. R. Arabnia, and L. Deligiannidis, Eds., Cham: Springer International Publishing, 2021, pp. 877–894. doi: 10.1007/978-3-030-71704-9_65. [32] G. Wilson and D. J. Cook, “A Survey of Unsupervised Deep Domain Adaptation,” ACM Trans. Intell. Syst. Technol., vol. 11, no. 5, p. 51:1-51:46, Jul. 2020, doi: 10.1145/3400066. [33] K. Sohn, S. Liu, G. Zhong, X. Yu, M.-H. Yang, and M. Chandraker, “Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos,” presented at the Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 3210–3218. Accessed: Jun. 07, 2024. [Online]. Available: https://openaccess.thecvf.com/content_iccv_2017/html/Sohn_Unsupervised_Domain_Adaptation_ICCV_2017_paper.html [34] Z. Yang, W. Chen, F. Wang, and B. Xu, “Unsupervised Domain Adaptation for Neural Machine Translation,” in 2018 24th International Conference on Pattern Recognition (ICPR), Aug. 2018, pp. 338–343. doi: 10.1109/ICPR.2018.8546053. [35] Z.-Y. Dou, J. Hu, A. Anastasopoulos, and G. Neubig, “Unsupervised Domain Adaptation for Neural Machine Translation with Domain-Aware Feature Embeddings,” Aug. 27, 2019, arXiv: arXiv:1908.10430. Accessed: Apr. 28, 2024. [Online]. Available: http://arxiv.org/abs/1908.10430 [36] L. O. Chua, “CNN: A Vision of Complexity,” Int. J. Bifurc. 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描述 碩士
國立政治大學
資訊科學系碩士在職專班
109971002
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109971002
資料類型 thesis
dc.contributor.advisor 李蔡彥<br>黃瀚萱zh_TW
dc.contributor.advisor Tsai-Yen Li<br>Hen-Hsen Huangen_US
dc.contributor.author (Authors) 莊格維zh_TW
dc.contributor.author (Authors) Chuang, Ko-Weien_US
dc.creator (作者) 莊格維zh_TW
dc.creator (作者) Chuang, Ko-Weien_US
dc.date (日期) 2024en_US
dc.date.accessioned 4-Sep-2024 14:35:42 (UTC+8)-
dc.date.available 4-Sep-2024 14:35:42 (UTC+8)-
dc.date.issued (上傳時間) 4-Sep-2024 14:35:42 (UTC+8)-
dc.identifier (Other Identifiers) G0109971002en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/153280-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學系碩士在職專班zh_TW
dc.description (描述) 109971002zh_TW
dc.description.abstract (摘要) 近年來，隨著大型語言模型（如Chat GPT）和通用人工智慧（AGI）及生成式人工智慧（GAI）模型的出現，這些技術引起了廣泛關注和研究。與此同時，人們對於人工智慧可能取代人類工作的擔憂日益增加，這種擔憂導致許多從業者試圖故意減緩人工智慧的發展，進而對人工智慧的進步構成挑戰。 在實際職場調查中，我們觀察到兩個值得關注的現象：第一，外部競爭者或廣告業者試圖利用模型的辨識弱點，對模型進行對抗性攻擊；第二，內部從業人員故意上傳錯誤的訓練資料，導致語言模型的準確率下降。 本研究旨在探討如何同時防範來自內部和外部的威脅，即對抗性標記者提供的錯誤標籤和外部使用者進行的對抗性攻擊，以確保模型的穩健性，並提升語言模型在雙重攻擊下的強韌性。 透過實驗和現實情境資料集的收集與分析，我們驗證了一系列演算法在抵抗對抗者上傳錯誤資料和辨識對抗攻擊方面的有效性。除此之外，我們的方法將基於圖像分類任務的先進雜訊標籤學習方法，替換成應用於基於自然語言處理方法，並證實了這些雜訊標籤學習方法在樣本特徵為圖像陣列以及文字向量時皆可以有效的實現，從而進一步證明了我們框架的價值和可靠性。 儘管本研究仍存在一些需要克服的限制，但我們相信，研究結果為未來可能面臨更多人為干擾的情境提供了更加穩健的防禦策略和構想。zh_TW
dc.description.abstract (摘要) In recent years, with the emergence of large language models such as Chat GPT, Artificial General Intelligence (AGI), and Generative Artificial Intelligence (GAI) models, these technologies have garnered widespread attention and research. Concurrently, concerns about AI potentially replacing human jobs have increased, leading many professionals to deliberately slow down AI development, posing challenges to its advancement. In our workplace investigation, we observed two noteworthy phenomena: first, external competitors or advertisers attempting to exploit model recognition weaknesses to conduct adversarial attacks; second, internal personnel intentionally uploading erroneous training data, causing a decline in language model accuracy. This study aims to explore how to simultaneously defend against threats from both internal and external sources, namely, adversarial annotators providing incorrect labels and external users conducting adversarial attacks, to ensure model robustness and enhance the resilience of language models under dual attacks. Through experiments and analysis of real-world datasets, we validated a series of algorithms that effectively counteract erroneous data uploads by adversaries and recognize adversarial attacks. Additionally, our approach adapts advanced noisy label learning methods, originally developed for image classification tasks, to natural language processing (NLP) methods, demonstrating that these noisy label learning methods are effective for both image arrays and text vectors. This further underscores the value and reliability of our framework. Despite some limitations that still need to be addressed, we believe our research results provide more robust defense strategies and insights for future scenarios potentially facing more human-induced interference.en_US
dc.description.tableofcontents 第1章 緒論 7 1.1 研究動機 7 1.2 研究問題定義 8 1.3 研究目標 9 1.4 研究貢獻 9 1.5 論文章節架構 10 第2章 相關研究 11 2.1 雜訊標籤學習（LEARNING FROM NOISY LABEL） 11 2.2 對抗意識標記（ADVERSARIAL AWARENESS LABEL） 15 2.3 群眾學習（LEARNING FROM CROWDSOURCING） 16 2.4 對抗性攻擊（ADVERSARIAL ATTACK） 18 2.5 對抗性訓練（ADVERSARIAL TRAINING） 18 2.6 領域自適應（DOMAIN ADAPTATION） 20 2.7 圖像識別模型轉換成自然語言處理模型 20 2.8 半監督式微調（SEMI-SUPERVISED FINE-TUNING） 22 第3章 演算法與模型 23 3.1 四階段演算法框架 23 3.2 語言模型 29 3.3 群眾學習方法 30 3.4 雜訊標籤學習模型 31 3.5 對抗性訓練模型 32 3.6 領域自適應模型 33 第4章 實驗設計與分析 35 4.1 實驗目標 35 4.2 實驗資料 35 4.3 實驗流程 37 4.3.1 驗證資料集 37 4.3.2 模擬對抗意識標記者 38 4.3.3 評估二、三階段演算法 40 4.3.4 驗證對抗性訓練 45 4.3.5 評估四階段演算法 48 4.4 實驗結果分析 50 第5章 結論 53 5.1 限制 53 5.2 結論 53 5.3 未來發展方向 54 參考文獻 55zh_TW
dc.format.extent 1622150 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109971002en_US
dc.subject (關鍵詞) 雜訊標籤學習zh_TW
dc.subject (關鍵詞) 群眾學習zh_TW
dc.subject (關鍵詞) 對抗意識攻擊zh_TW
dc.subject (關鍵詞) 對抗訓練zh_TW
dc.subject (關鍵詞) Noisy label learningen_US
dc.subject (關鍵詞) Learning from Crowdsourcingen_US
dc.subject (關鍵詞) Adversarial-awareness-attacken_US
dc.subject (關鍵詞) Adversarial trainingen_US
dc.title (題名) 在自然語言處理中同時抵禦內部雜訊與外部攻擊的雙向防禦框架zh_TW
dc.title (題名) A Dual Defense Framework Against Internal Noise and External Attacks in Natural Language Processingen_US
dc.type (資料類型) thesisen_US
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