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Wanet\u2013imperceptible warping-based backdoor attack. arXiv:2102.10369. 2021."},{"key":"ref16","unstructured":"Niu Y, He S, Wei Q, Wu Z, Liu F, Feng L. Bdetclip: multimodal prompting contrastive test-time backdoor detection. arXiv:2405.15269. 2024."},{"key":"ref17","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"631","article-title":"When nas meets robustness: in search of robust architectures against adversarial attacks","author":"Guo","year":"2020"},{"key":"ref18","series-title":"2022 International Joint Conference on Neural Networks (IJCNN)","first-page":"1","article-title":"Effective, efficient and robust neural architecture search","author":"Yue","year":"2022"},{"key":"ref19","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"4466","article-title":"Enhancing fine-tuning based backdoor defense with sharpness-aware minimization","author":"Zhu","year":"2023"},{"key":"ref20","series-title":"Computer Vision\u2013ECCV 2020 Workshops; 2020 Aug 23\u201328; Glasgow, UK: Springer","first-page":"55","article-title":"Deep k-nn defense against clean-label data poisoning attacks","author":"Peri","year":"2020"},{"key":"ref21","series-title":"Proceedings of the ACM\/IEEE 12th International Conference on Cyber-Physical Systems","first-page":"67","article-title":"Real-time detectors for digital and physical adversarial inputs to perception systems","author":"Kantaros","year":"2021"},{"key":"ref22","unstructured":"Wen J. HOLMES: to detect adversarial examples with multiple detectors. arXiv:2405.19956. 2024."},{"key":"ref23","unstructured":"Stein K, Mahyari AA, Francia G, El-Sheikh E. Proactive adversarial defense: harnessing prompt tuning in vision-language models to detect unseen backdoored images. arXiv:2412.08755. 2024."},{"key":"ref24","series-title":"European Conference on Computer Vision","first-page":"401","article-title":"Augmented neural fine-tuning for efficient backdoor purification","author":"Karim","year":"2024"},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"Xu Y, Gu Y, Sakurai K. PAD-FT: a lightweight defense for backdoor attacks via data purification and fine-tuning. arXiv:2409.12072. 2024.","DOI":"10.1145\/3733826.3762677"},{"key":"ref26","unstructured":"Chen B, Carvalho W, Baracaldo N, Ludwig H, Edwards B, Lee T, et al. Detecting backdoor attacks on deep neural networks by activation clustering. arXiv:1811.03728. 2018."},{"key":"ref27","article-title":"Spectral signatures in backdoor attacks","volume":"31","author":"Tran","year":"2018","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref28","series-title":"Proceedings of the 38th International Conference on Machine Learning","first-page":"4129","article-title":"SPECTRE: defending against backdoor attacks using robust statistics","author":"Hayase","year":"2021"},{"key":"ref29","series-title":"30th USENIX Security Symposium (USENIX Security 21)","first-page":"1541","article-title":"Demon in the variant: statistical analysis of DNNs for robust backdoor contamination detection","author":"Tang","year":"2021"},{"key":"ref30","doi-asserted-by":"crossref","unstructured":"Ma W, Wang D, Sun R, Xue M, Wen S, Xiang Y. The \u201cBeatrix\u201d resurrections: robust backdoor detection via gram matrices. arXiv:2209.11715. 2022.","DOI":"10.14722\/ndss.2023.23069"},{"key":"ref31","doi-asserted-by":"crossref","unstructured":"Li Y, He J, Huang H, Sun J, Ma X. Shortcuts everywhere and nowhere: exploring multi-trigger backdoor attacks. arXiv:2401.15295. 2024.","DOI":"10.1109\/TDSC.2025.3605597"},{"key":"ref32","unstructured":"Lake BM. Towards more human-like concept learning in machines: compositionality, causality, and learning-to-learn. massachusetts institute of technology; 2014. [cited 2025 Jul 10]. Available from: http:\/\/hdl.handle.net\/1721.1\/95856."},{"key":"ref33","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"11467","article-title":"Hierarchical disentanglement of discriminative latent features for zero-shot learning","author":"Tong","year":"2019"},{"key":"ref34","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"8712","article-title":"Semantics disentangling for generalized zero-shot learning","author":"Chen","year":"2021"},{"key":"ref35","first-page":"1966","article-title":"Generalized zero-shot learning via disentangled representation","volume":"35","author":"Li","year":"2021","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref36","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"15315","article-title":"Learning attention as disentangler for compositional zero-shot learning","author":"Hao","year":"2023 Jun 18\u201324"},{"key":"ref37","unstructured":"Stein K, Mahyari A, Francia G, El-Sheikh E. Visual adaptive prompting for compositional zero-shot learning. arXiv:2502.20292. 2025."},{"key":"ref38","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"9326","article-title":"Siamese contrastive embedding network for compositional zero-shot learning","author":"Li","year":"2022"},{"key":"ref39","first-page":"2652","article-title":"Retrieval-augmented primitive representations for compositional zero-shot learning","volume":"38","author":"Jing","year":"2024","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref40","unstructured":"Nayak NV, Yu P, Bach SH. Learning to compose soft prompts for compositional zero-shot learning. arXiv:2204.03574. 2022."},{"key":"ref41","series-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","first-page":"5774","article-title":"GIPCOL: graph-injected soft prompting for compositional zero-shot learning","author":"Xu","year":"2024"},{"key":"ref42","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"23560","article-title":"Decomposed soft prompt guided fusion enhancing for compositional zero-shot learning","author":"Lu","year":"2023"},{"key":"ref43","first-page":"2088","article-title":"Invisible backdoor attacks on deep neural networks via steganography and regularization","volume":"18","author":"Li","year":"2020","journal-title":"IEEE Trans Dependable Secure Comput"},{"key":"ref44","unstructured":"Wu B, Wei S, Zhu M, Zheng M, Zhu Z, Zhang M, et al. Defenses in adversarial machine learning: a survey. arXiv:2312.08890. 2023."},{"key":"ref45","unstructured":"Avd O, Li Y, Vinyals O. Representation learning with contrastive predictive coding. arXiv:1807.03748. 2018."},{"key":"ref46","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"16816","article-title":"Conditional prompt learning for vision-language models","author":"Zhou","year":"2022 Jun 17\u201324"},{"key":"ref47","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1007\/s11263-022-01653-1","article-title":"Learning to prompt for vision-language models","volume":"130","author":"Zhou","year":"2022","journal-title":"Int J Comput Vis"},{"key":"ref48","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"19113","article-title":"Maple: multi-modal prompt learning","author":"Khattak","year":"2023 Jun 17\u201324"},{"key":"ref49","unstructured":"Krizhevsky A, Hinton G. Learning multiple layers of features from tiny images. 2009. [cited 2025 Jul 10]. Available from: https:\/\/www.cs.utoronto.ca\/."},{"key":"ref50","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.neunet.2012.02.016","article-title":"Man vs. computer: benchmarking machine learning algorithms for traffic sign recognition","volume":"32","author":"Stallkamp","year":"2012","journal-title":"Neural Netw"},{"key":"ref51","series-title":"Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security","first-page":"2041","article-title":"Latent backdoor attacks on deep neural networks","author":"Yao","year":"2019 Nov 06"},{"key":"ref52","article-title":"Pytorch: an imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref53","unstructured":"Kingma DP. Adam: a method for stochastic optimization. arXiv:1412.6980. 2014."},{"article-title":"Towards deep learning models resistant to adversarial attacks","series-title":"6th International Conference on Learning Representations, ICLR 2018","author":"Madry","key":"ref54"},{"article-title":"Explaining and harnessing adversarial examples","series-title":"3rd International Conference on Learning Representations, ICLR 2015","author":"Goodfellow","key":"ref55"}],"container-title":["Computers, Materials & Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-85-1\/TSP_CMC_68201\/TSP_CMC_68201.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T02:04:11Z","timestamp":1763345051000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v85n1\/63578"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":55,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.068201","relation":{},"ISSN":["1546-2226"],"issn-type":[{"type":"electronic","value":"1546-2226"}],"subject":[],"published":{"date-parts":[[2025]]}}}