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| 题名: | 強化深度學習對於自然語言處理的強韌度-以假新聞偵測為例
Enhancing Deep Learning Robustness for Nature Language Processing : Fake News Detection as an Example |
| 作者: | 余昊祥
Yu, Hao-Hsiang |
| 贡献者: | 胡毓忠
Hu,Yuh-Jong
余昊祥
Yu, Hao-Hsiang |
| 关键词: | 假新聞偵測
對抗式攻擊
假新聞偵測
Fake news detection
Adversarial attack
Adversarial Defence
TextFooler |
| 日期: | 2022 |
| 上传时间: | 2022-09-02 15:47:00 (UTC+8) |
| 摘要: | 因為互聯網與社群媒體的推波助瀾，網路新聞已經成為重要的新聞來源。近幾年因為對抗式攻擊研究議題興起，使得運用深度學習模型偵測假新聞的辨識正確性備受挑戰。
本研究嘗試透過 TFIDF、TextRank、KeyBERT 等文字探勘方法，以及測試模型輸出 LogitOut 方法，找到文本中容易受到 TextFooler 擾動的標的，再將找到的關鍵單詞進行同義詞置換生成模擬對抗樣本，透過對抗式訓練的方式強化 BERT 假新聞判別器對於 TextFooler 攻擊的強韌度。實驗結果發現：(1) 文字探勘方法中 KeyBERT 較能找出 TextFooler 攻擊單詞，而模型輸出 LogitOut 又明顯優於文字探勘方法。(2) 關鍵字搜尋方法對於 TextFooler 攻擊單詞命中率越高，越能透過同義詞置換生成模擬對抗範例，並藉由訓練模擬對抗範例後提升 BERT 假新聞判別器對於 TextFooler 對抗式攻擊的強韌度。
In recent years, the research of adversarial attack has emerged, making the fake news detection by using deep learning method challenging again.
In this study, we try to increase the robustness of BERT fake news detector against TextFooler by training simulated adversarial samples. To generate simulated adversarial samples, we use both text mining method such as TFIDF, TextRank, KeyBERT and method by testing model ouput (LogitOut) combining with synonyms replacement strategy. The experimental results found that (1) KeyBERT is more capable of identifying the attacked subject by TextFooler comparing with other text mining methods, and testing model
output(LogitOut) method is much better than text mining methods. (2) The robustness of BERT fake news detector against TextFooler can be improved after adding the simulated adversarial examples mentioned above. |
| 參考文獻: | [1] Nic Newman, Richard Fletcher, and David A. L. Levy, et al. digital-newsreport2016. Digital Journalism. https://reutersinstitute.politics.ox.ac.uk/
our-research/digital-news-report-2016, 2016.
[2] Edson C., Tandoc Jr., and Zheng Wei Lim, et al. Defining fake news. Digital Jour-nalism. https://doi.org/10.1080/21670811.2017.1360143, 2018.
[3] Ashish Vaswani, Noam M. Shazeer, and Niki Parmar, et al. Attention is all you need.
arXiv preprint arXiv:1706.03762, 2017.
[4] Jacob Devlin, MingWei Chang, and Kenton Lee, et al. Bert: Pretraining of deep bidirectional transformers for language understanding. arXiv preprint
arXiv:1810.04805, 2019.
[5] Haoming Guo, Tianyi Huan, and Huixuan Huang, et al. Detecting covid19 conspir-acy theories with transformers and tfidf. arXiv preprint arXiv:2205.00377, 2022.
[6] Jin Di, Jin Zhijing, and Zhou Joey Tianyi, et al. Is bert really robust? natural language attack on text classification and entailment. arXiv preprint arXiv:1907.11932, 2019.
[7] Shilin Qiu, Qihe Liu, and Shijie Zhou, et al. Adversarial attack and defense tech-nologies in natural language processing: A survey. Neurocomputing, 2022.
[8] Ji Gao, Jack Lanchantin, and Mary Lou Soffa, et al. Blackbox generation of adver-sarial text sequences to evade deep learning classifiers. In 2018 IEEE Security and
Privacy Workshops (SPW). IEEE, 2018.
[9] Robin Jia, Percy Liang. Adversarial examples for evaluating reading comprehension systems. arXiv preprint arXiv:1707.07328, 2017.
[10] Zhihong Shao, Zitao Liu, and Jiyong Zhang, et al. Advexpander: Generating natu-ral language adversarial examples by expanding text. IEEE/ACM Transactions on
Audio, Speech, and Language Processing, 2022.
[11] Daniel Matthew Cer, Yinfei Yang, and Shengyi Kong, et al. Universal sentence encoder. arXiv preprint arXiv:1803.11175, 2018.
[12] Mein Gunnar, Hartman Kevin, Morris Andrew. Firebert: Hardening bertbased clas-sifiers against adversarial attack. arXiv preprint arXiv:2008.04203, 2020.
[13] Page Lawrence, Brin Sergey, and Motwani Rajeev, et al. The pagerank citation ranking: Bringing order to the web. Technical report, Stanford InfoLab, 1999.
[14] Mihalcea Rada, Tarau Paul. Textrank: Bringing order into text. In Proceedings of the 2004 conference on empirical methods in natural language processing, 2004.
[15] Grootendorst, Maarten. Keybert: Minimal keyword extraction with bert. [Internet].
Available: https://maartengr. github. io/KeyBERT/index. html, 2020.
[16] Nikola Mrksic, Diarmuid Ó Séaghdha, and Blaise Thomson, et al. Counterfitting word vectors to linguistic constraints. In NAACL, 2016. |
| 描述: | 碩士
國立政治大學
資訊科學系碩士在職專班
106971008 |
| 資料來源: | http://thesis.lib.nccu.edu.tw/record/#G0106971008 |
| 数据类型: | thesis |
| DOI: | 10.6814/NCCU202201381 |
| 显示于类别: | [資訊科學系碩士在職專班] 學位論文
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