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[80] Kurita K, Michel P, Neubig G. Weight poisoning attacks on pre-trained models. arXiv:2004.06660, 2020.
[81] Wei CA, Lee Y, Chen K, Meng GZ, Lv PZ. Aliasing backdoor attacks on pre-trained models. In: Proc. of the 32nd USENIX Security
Symp. Anaheim: USENIX Association, 2023. 2707–2724.
[82] Li HL, Wang YF, Xie XF, Liu Y, Wang SQ, Wan RJ, Chau LP, Kot AC. Light can hack your face! Black-box backdoor attack on face
recognition systems. arXiv:2009.06996, 2020.
[83] Rakin AS, He ZZ, Fan DL. TBT: Targeted neural network attack with bit Trojan. In: Proc. of the 2020 IEEE/CVF Conf. on Computer
Vision and Pattern Recognition. Seattle: IEEE, 2020. 13195–13204. [doi: 10.1109/CVPR42600.2020.01321]
[84] Chen HL, Fu C, Zhao JS, Koushanfar F. ProFlip: Targeted Trojan attack with progressive bit flips. In: Proc. of the 2021 IEEE/CVF Int’l
Conf. on Computer Vision (ICCV). Montreal: IEEE, 2021. 7698–7707. [doi: 10.1109/ICCV48922.2021.00762]
[85] Bagdasaryan E, Shmatikov V. Blind backdoors in deep learning models. In: Proc. of the 30th USENIX Security Symp. USENIX
Association, 2021. 1505–1521.
[86] Saha A, Tejankar A, Koohpayegani SA, Pirsiavash H. Backdoor attacks on self-supervised learning. In: Proc. of the 2022 IEEE/CVF
Conf. on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022. 13327–13336. [doi: 10.1109/CVPR52688.2022.01298]
[87] Hou RT, Huang T, Yan HY, Ke LS, Tang WX. A stealthy and robust backdoor attack via frequency domain transform. World Wide
Web, 2023, 26(5): 2767–2783. [doi: 10.1007/s11280-023-01153-3]
[88] Wang T, Yao Y, Xu F, An SW, Tong HH, Wang T. An invisible black-box backdoor attack through frequency domain. In: Proc. of the
17th European Conf. on Computer Vision. Tel Aviv: Springer, 2022. 396–413. [doi: 10.1007/978-3-031-19778-9_23]
[89] Xiang Z, Miller DJ, Chen SH, Li X, Kesidis G. A backdoor attack against 3D point cloud classifiers. In: Proc. of the 2021 IEEE/CVF Int’l
Conf. on Computer Vision (ICCV). Montreal: IEEE, 2021. 7577–7587. [doi: 10.1109/ICCV48922.2021.00750]
[90] Gao KF, Bai JW, Wu BY, Ya MX, Xia ST. Imperceptible and robust backdoor attack in 3D point cloud. IEEE Trans. on Information
Forensics and Security, 2024, 19: 1267–1282. [doi: 10.1109/TIFS.2023.3333687]
[91] Li SF, Liu H, Dong T, Zhao BZH, Xue MH, Zhu HJ, Lu JL. Hidden backdoors in human-centric language models. In: Proc. of the 2021
ACM SIGSAC Conf. on Computer and Communications Security. ACM, 2021. 3123–3140. [doi: 10.1145/3460120.3484576]
[92] Li ZC, Li PJ, Sheng X, Yin CC, Zhou L. IMTM: Invisible multi-trigger multimodal backdoor attack. In: Proc. of the 12th National CCF
Conf. on Natural Language Processing and Chinese Computing. Foshan: Springer, 2023. 533–545. [doi: 10.1007/978-3-031-44696-
2_42]
[93] Mei K, Li Z, Wang ZT, Zhang Y, Ma SQ. NOTABLE: Transferable backdoor attacks against prompt-based NLP models. In: Proc. of
the 61st Annual Meeting of the Association for Computational Linguistics, Vol. 1 (Long Papers). Toronto: ACL, 2023. 15551–15565.
[doi: 10.18653/v1/2023.acl-long.867]
[94] Barni M, Kallas K, Tondi B. A new backdoor attack in CNNS by training set corruption without label poisoning. In: Proc. of the 2019
IEEE Int’l Conf. on Image Processing (ICIP). Taipei: IEEE, 2019. 101–105. [doi: 10.1109/ICIP.2019.8802997]
[95] Zhang Q, Ding YF, Tian YQ, Guo JM, Yuan M, Jiang Y. AdvDoor: Adversarial backdoor attack of deep learning system. In: Proc. of
the 30th ACM SIGSOFT Int’l Symp. on Software Testing and Analysis. Virtual: ACM, 2021. 127–138. [doi: 10.1145/3460319.
3464809]
[96] Shafahi A, Huang WR, Najibi M, Suciu O, Studer C, Dumitras T, Goldstein T. Poison frogs! Targeted clean-label poisoning attacks on
neural networks. In: Proc. of the 32nd Conf. on Neural Information Processing Systems. Montréal: Curran Associates Inc., 2018.
6106–6116.
[97] D’Onghia M, Di Cesare F, Gallo L, Carminati M, Polino M, Zanero S. Lookin’ out my backdoor! Investigating backdooring attacks
against DL-driven malware detectors. In: Proc. of the 16th ACM Workshop on Artificial Intelligence and Security. Copenhagen: ACM,
2023. 209–220. [doi: 10.1145/3605764.3623919]
[98] Li YZ, Li YM, Wu BY, Li LK, He R, Lyu SW. Invisible backdoor attack with sample-specific triggers. In: Proc. of the 2021 IEEE/CVF
Int’l Conf. on Computer Vision (ICCV). Montreal: IEEE, 2021. 16443–16452. [doi: 10.1109/ICCV48922.2021.01615]
[99] Ma BH, Zhao C, Wang DJ, Meng B. DIHBA: Dynamic, invisible and high attack success rate boundary backdoor attack with low
poison ratio. Computers & Security, 2023, 129: 103212. [doi: 10.1016/j.cose.2023.103212]
[100] Chen B, Carvalho W, Baracaldo N, Ludwig H, Edwards B, Lee T, Molly I, Sricastava B. Detecting backdoor attacks on deep neural
networks by activation clustering. arXiv:1811.03728, 2018.
[101] Tran B, Li J, Madry A. Spectral signatures in backdoor attacks. In: Proc. of the 32nd Int’l Conf. on Neural Information Processing
Systems. Montreal: Curran Associates Inc., 2018. 8011–8021.
[102] Pan MZ, Zeng Y, Lyu LJ, Lin X, Jia RX. ASSET: Robust backdoor data detection across a multiplicity of deep learning paradigms. In:
Proc. of the 32nd USENIX Security Symp. Anaheim: USENIX Association, 2023. 2725–2742.
[103] Ma WL, Wang DR, Sun RX, Xue MH, Wen S, Xiang Y. The “Beatrix” Resurrections: Robust backdoor detection via gram matrices. In:

