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Self-supervised learning (SSL) is a prevalent approach for encoding data representations.
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Liu, Y., Lee, W.C., Tao, G., Ma, S., Aafer, Y., Zhang, X.: Abs: Scanning neural networks for back-doors by artificial brain stimulation. In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security. pp. 1265–1282 (2019)
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Wang, B., Yao, Y., Shan, S., Li, H., Viswanath, B., Zheng, H., Zhao, B.Y.: Neural cleanse: Identifying and mitigating backdoor attacks in neural networks. In: Proceedings of the IEEE Symposium on Security and Privacy (IEEE S&P). San Francisco, CA (2019)
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Kolouri, S., Saha, A., Pirsiavash, H., Hoffmann, H.: Universal litmus patterns: Revealing backdoor attacks in cnns. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 301–310 (2020)
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Liu, Y., Jin, M., Pan, S., Zhou, C., Zheng, Y., Xia, F., Yu, P.: Graph self-supervised learning: A survey. IEEE Transactions on Knowledge and Data Engineering (2022)
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Wu, J., Zhang, T., Zha, Z.J., Luo, J., Zhang, Y., Wu, F.: Self-supervised domain-aware generative network for generalized zero-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12767–12776 (2020)
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Chen, X., He, K.: Exploring simple siamese representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 15750–15758 (2021)
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Krishnan, R., Rajpurkar, P., Topol, E.J.: Self-supervised learning in medicine and healthcare. Nature Biomedical Engineering pp. 1–7 (2022)
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Al Ghanim, M., Santriaji, M., Lou, Q., Solihin, Y.: Trojbits: A hardware aware inference-time attack on transformer-based language models. In: ECAI 2023, pp. 60–68. IOS Press (2023)
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Feng, S., Tao, G., Cheng, S., Shen, G., Xu, X., Liu, Y., Zhang, K., Ma, S., Zhang, X.: Detecting backdoors in pre-trained encoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16352–16362 (2023)
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Lou, Q., Liu, Y., Feng, B.: Trojtext: Test-time invisible textual trojan insertion. In: The Eleventh International Conference on Learning Representations (2023)
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Tejankar, A., Sanjabi, M., Wang, Q., Wang, S., Firooz, H., Pirsiavash, H., Tan, L.: Defending against patch-based backdoor attacks on self-supervised learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12239–12249 (2023)
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Yang, H., Yang, R., Cai, H., Zhang, X., Pei, Q., Wang, S., Yan, H.: Ssl-abd: An adversarial defense method against backdoor attacks in self-supervised learning. In: International Conference on Artificial Intelligence Security and Privacy. pp. 456–467. Springer (2023)
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Zheng, M., Lou, Q., Jiang, L.: Trojvit: Trojan insertion in vision transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4025–4034 (2023)
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