Abstract
Backdoor attacks, which manipulate model output, have garnered significant attention from researchers. However, some existing word-level backdoor attack methods in NLP models are difficult to defend effectively due to their concealment and diversity. These covert attacks use two words that appear similar to the naked eye but will be mapped to different word vectors by the NLP model as a way of bypassing existing defenses. To address this issue, we propose incorporating triple metric learning into the standard training phase of NLP models to defend against existing word-level backdoor attacks. Specifically, metric learning is used to minimize the distance between vectors of similar words while maximizing the distance between them and vectors of other words. Additionally, given that metric learning may reduce a model’s sensitivity to semantic changes caused by subtle perturbations, we added contrastive learning after the model’s standard training. Experimental results demonstrate that our method performs well against the two most stealthy existing word-level backdoor attacks.
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Acknowledgements
This work was supported in part by Jiangsu Province Modern Education Technology Research 2021 Smart Campus Special Project “Research and Practice of Data Security System in College Smart Campus Construction” (2021-R-96776); Jiangsu Province University Philosophy and Social Science Research Major Project “Higher Vocational College Ideological and Political Science” “Research on the Construction of Class Selective Compulsory Courses” (2022SJZDSZ011); Research Project of Nanjing Industrial Vocational and Technical College (2020SKYJ03); 2022 “Research on the Teaching Reform of High-quality Public Courses in Jiangsu Province Colleges and Universities” --The new era of labor education courses in Jiangsu higher vocational colleges Theoretical and practical research.
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Yang, S., Li, Q., Lian, Z., Wang, P., Hou, J. (2024). MIC: An Effective Defense Against Word-Level Textual Backdoor Attacks. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14452. Springer, Singapore. https://doi.org/10.1007/978-981-99-8076-5_1
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