Lightweight Language Agnostic Data Sanitization Pipeline for Dealing with Homoglyphs in Code-Mixed Languages
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-58495-4_11
Reference14 articles.
1. Boucher, N., Shumailov, I., Anderson, R., Papernot, N.: Bad characters: imperceptible NLP attacks; bad characters: imperceptible NLP attacks. In: 2022 IEEE Symposium on Security and Privacy (SP) (2022). https://doi.org/10.1109/SP46214.2022.00045
2. Majumder, M., Rahman, M., Iqbal, A., Rahman, M.S.: Convolutional neural network based ensemble approach for homoglyph recognition. Math. Comput. Appl. 25, 71 (2020). https://doi.org/10.3390/mca25040071
3. Deng, P., Linsky, C., Wright, M.: Weaponizing Unicodes with Deep Learning – Identifying Homoglyphs with Weakly Labeled Data (2020). https://doi.org/10.1109/ISI49825.2020.9280538
4. Tolstosheyev, R., Sadykhzadeh, A.: Methods of detecting and recovering after homoglyph attacks. Research Gate (2021). https://doi.org/10.13140/RG.2.2.31410.27846
5. Li, S., et al.: Hidden backdoors in human-centric language models. In: Proceedings of the ACM Conference on Computer and Communications Security, pp. 3123–3140 (2021). https://doi.org/10.1145/3460120.3484576
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