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                      effective NLP backdoor defense. In: Proc. of the 39th Int’l Conf. on Machine Learning. Baltimore: ICML, 2022. 19879–19892.
                 [127]  Xu XJ, Wang Q, Li HC, Borisov N, Gunter CA, Li B. Detecting AI Trojans using meta neural analysis. In: Proc. of the 2021 IEEE
                      Symp. on Security and Privacy. San Francisco: IEEE, 2021. 103–120. [doi: 10.1109/SP40001.2021.00034]
                 [128]  Kolouri S, Saha A, Pirsiavash H, Hoffmann H. Universal litmus patterns: Revealing backdoor attacks in CNNs. In: Proc. of the 2020
                      IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020. 298–307. [doi: 10.1109/CVPR42600.2020.00038]
                 [129]  Wang JL, Zhang ZY, Wang MQ, Qiu H, Zhang TW, Li Q, Li ZP, Wei T, Zhang C. Aegis: Mitigating targeted bit-flip attacks against
                      deep neural networks. In: Proc. of the 32nd USENIX Security Symp. Anaheim: USENIX Association, 2023. 2329–2346.
                 [130]  Xiang C, Bhagoji AN, Sehwag V, Mittal P. PatchGuard: A provably robust defense against adversarial patches via small receptive fields
                      and masking. In: Proc. of the 30th USENIX Security Symp. USENIX Association, 2021. 2237–2254.
                 [131]  Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc. of the IEEE, 1998, 86(11):
                      2278–2324. [doi: 10.1109/5.726791]
                 [132]  Krizhevsky A, Hinton G. Learning multiple layers of features from tiny images. Technical Report, TR-2009, Toronto: University of
                      Toronto, 2009.
                 [133]  Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. ImageNet: A large-scale hierarchical image database. In: Proc. of the 2009 IEEE
                      Conf. on Computer Vision and Pattern Recognition. Miami: IEEE, 2009. 248–255. [doi: 10.1109/CVPR.2009.5206848]
                 [134]  Stallkamp J, Schlipsing M, Salmen J, Igel C. The german traffic sign recognition benchmark: A multi-class classification competition.
                      In: Proc. of the 2011 Int’l Joint Conf. on Neural Networks. San Jose: IEEE, 2011. 1453–1460. [doi: 10.1109/IJCNN.2011.6033395]
                 [135]  Cao Q, Shen L, Xie WD, Parkhi OM, Zisserman A. VGGFace2: A dataset for recognising faces across pose and age. In: Proc. of the
                      13th IEEE Int’l Conf. on Automatic Face & Gesture Recognition (FG). Xi’an: IEEE, 2018. 67–74. [doi: 10.1109/FG.2018.00020]
                 [136]  Kumar N, Berg AC, Belhumeur PN, Nayar SK. Attribute and simile classifiers for face verification. In: Proc. of the 12th IEEE Int’l
                      Conf. on Computer Vision. Kyoto: IEEE, 2009. 365–372. [doi: 10.1109/ICCV.2009.5459250]
                 [137]  Liu ZW, Luo P, Wang XG, Tang XO. Deep learning face attributes in the wild. In: Proc. of the 2015 IEEE Int’l Conf. on Computer
                      Vision (ICCV). Santiago: IEEE, 2015. 3730–3738. [doi: 10.1109/ICCV.2015.425]
                 [138]  Maas AL, Daly RE, Pham PT, Huang D, Ng AY, Potts C. Learning word vectors for sentiment analysis. In: Proc. of the 49th Annual
                      Meeting of the Association for Computational Linguistics: Human Language Technologies. Potland: ACL, 2011. 142–150.
                 [139]  Socher R, Perelygin A, Wu J, Chuang J, Manning CD, Ng A, Potts C. Recursive deep models for semantic compositionality over a
                      sentiment treebank. In: Proc. of the 2013 Conf. on Empirical Methods in Natural Language Processing (EMNLP). Seattle: ACL, 2013.
                      1631–1642.
                 [140]  Yelp dataset. 2024. https://www.yelp.com/dataset
                 [141]  Rajpurkar P, Zhang J, Lopyrev K, Liang P. SQuAD: 100,000+ Questions for machine comprehension of text. In: Proc. of the 2016 Conf.
                      on Empirical Methods in Natural Language Process. Austin: ACL, 2016. 2383–2392. [doi: 10.18653/v1/D16-1264]
                 [142]  Xiao  H,  Rasul  K,  Vollgraf  R.  Fashion-MNIST:  A  novel  image  dataset  for  benchmarking  machine  learning  algorithms.
                      arXiv:1708.07747, 2017.
                 [143]  Parkhi OM, Vedaldi A, Zisserman A, Jawahar CV. Cats and dogs. In: Proc. of the 2012 IEEE Conf. on Computer Vision and Pattern
                      Recognition. Providence: IEEE, 2012. 3498–3505. [doi: 10.1109/CVPR.2012.6248092]
                 [144]  Nilsback  ME,  Zisserman  A.  Automated  flower  classification  over  a  large  number  of  classes.  In:  Proc.  of  the  6th  Indian  Conf.  on
                      Computer Vision, Graphics & Image Processing. Bhubaneswar: IEEE, 2008. 722–729. [doi: 10.1109/ICVGIP.2008.47]
                 [145]  Fei-Fei L, Fergus R, Perona P. Learning generative visual models from few training examples: An incremental Bayesian approach tested
                      on 101 object categories. In: Proc. of the 2004 IEEE Conf. on Computer Vision and Pattern Recognition Workshop. Washington: IEEE,
                      2004. 178. [doi: 10.1109/CVPR.2004.383]
                 [146]  Griffin G, Holub A, Perona P. Caltech-256 object category dataset (public). Technical Report, CNS-TR-2007-001, California Institute of
                      Technology, 2007. https://authors.library.caltech.edu/records/5sv1j-ytw97
                 [147]  Coates A, Ng AY, Lee H. An analysis of single-layer networks in unsupervised feature learning. In: Proc. of the 14th Int’l Conf. on
                      Artificial Intelligence and Statistics. 2011. 215–223.
                 [148]  Møgelmose A, Liu DR, Trivedi MM. Traffic sign detection for U.S. roads: Remaining challenges and a case for tracking. In: Proc. of the
                      17th  Int’l  IEEE  Conf.  on  Intelligent  Transportation  Systems  (ITSC).  Qingdao:  IEEE,  2014.  1394–1399.  [doi: 10.1109/ITSC.2014.
                      6957882]
                 [149]  Huang L. Chinese traffic sign database. 2024. http://www.nlpr.ia.ac.cn/pal/trafficdata/recognition.html
                 [150]  Timofte  R,  Zimmermann  K,  van  Gool  L.  Multi-view  traffic  sign  detection,  recognition,  and  3D  localisation.  Machine  Vision  and
                      Applications, 2014, 25(3): 633–647. [doi: 10.1007/s00138-011-0391-3]
                 [151]  Sengupta S, Chen JC, Castillo C, Patel VM, Chellappa R, Jacobs DW. Frontal to profile face verification in the wild. In: Proc. of the
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