{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T08:40:29Z","timestamp":1742978429512,"version":"3.40.3"},"publisher-location":"Cham","reference-count":80,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031730207"},{"type":"electronic","value":"9783031730214"}],"license":[{"start":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T00:00:00Z","timestamp":1732147200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T00:00:00Z","timestamp":1732147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-73021-4_6","type":"book-chapter","created":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T09:15:16Z","timestamp":1732094116000},"page":"89-107","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Flatness-Aware Sequential Learning Generates Resilient Backdoors"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2501-8271","authenticated-orcid":false,"given":"Hoang","family":"Pham","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2615-7316","authenticated-orcid":false,"given":"The-Anh","family":"Ta","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3120-4036","authenticated-orcid":false,"given":"Anh","family":"Tran","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1610-8206","authenticated-orcid":false,"given":"Khoa D.","family":"Doan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,21]]},"reference":[{"key":"6_CR1","unstructured":"Ahn, H., Cha, S., Lee, D., Moon, T.: Uncertainty-based continual learning with adaptive regularization. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: learning what (not) to forget. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 139\u2013154 (2018)","DOI":"10.1007\/978-3-030-01219-9_9"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Bang, J., Kim, H., Yoo, Y., Ha, J.W., Choi, J.: Rainbow memory: continual learning with a memory of diverse samples. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8218\u20138227 (2021)","DOI":"10.1109\/CVPR46437.2021.00812"},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Barni, M., Kallas, K., Tondi, B.: A new backdoor attack in CNNs by training set corruption without label poisoning. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 101\u2013105. IEEE (2019)","DOI":"10.1109\/ICIP.2019.8802997"},{"key":"6_CR5","unstructured":"Brown, T., et al.: Language models are few-shot learners. In: Advances in Neural Information Processing Systems, vol. 33, pp. 1877\u20131901 (2020)"},{"key":"6_CR6","unstructured":"Carlini, N., et\u00a0al.: Extracting training data from large language models. In: 30th USENIX Security Symposium (USENIX Security 2021), pp. 2633\u20132650 (2021)"},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 39\u201357. IEEE (2017)","DOI":"10.1109\/SP.2017.49"},{"key":"6_CR8","unstructured":"Chai, S., Chen, J.: One-shot neural backdoor erasing via adversarial weight masking. In: Advances in Neural Information Processing Systems, vol. 35, pp. 22285\u201322299 (2022)"},{"key":"6_CR9","unstructured":"Chaudhry, A., Ranzato, M., Rohrbach, M., Elhoseiny, M.: Efficient lifelong learning with a-GEM. In: International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=Hkf2_sC5FX"},{"key":"6_CR10","unstructured":"Chen, X., Liu, C., Li, B., Lu, K., Song, D.: Targeted backdoor attacks on deep learning systems using data poisoning. arXiv preprint arXiv:1712.05526 (2017)"},{"key":"6_CR11","doi-asserted-by":"publisher","first-page":"138872","DOI":"10.1109\/ACCESS.2019.2941376","volume":"7","author":"J Dai","year":"2019","unstructured":"Dai, J., Chen, C., Li, Y.: A backdoor attack against LSTM-based text classification systems. IEEE Access 7, 138872\u2013138878 (2019)","journal-title":"IEEE Access"},{"key":"6_CR12","unstructured":"Deng, D., Chen, G., Hao, J., Wang, Q., Heng, P.A.: Flattening sharpness for dynamic gradient projection memory benefits continual learning. In: Advances in Neural Information Processing Systems, vol. 34, pp. 18710\u201318721 (2021)"},{"key":"6_CR13","unstructured":"Doan, K., Lao, Y., Li, P.: Backdoor attack with imperceptible input and latent modification. In: Ranzato, M., Beygelzimer, A., Dauphin, Y.N., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, 6\u201314 December 2021, Virtual, pp. 18944\u201318957 (2021). https:\/\/proceedings.neurips.cc\/paper\/2021\/hash\/9d99197e2ebf03fc388d09f1e94af89b-Abstract.html"},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"Doan, K., Lao, Y., Zhao, W., Li, P.: LIRA: learnable, imperceptible and robust backdoor attacks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 11966\u201311976 (2021)","DOI":"10.1109\/ICCV48922.2021.01175"},{"key":"6_CR15","unstructured":"Doan, K.D., Lao, Y., Li, P.: Marksman backdoor: backdoor attacks with arbitrary target class. In: Advances in Neural Information Processing Systems, vol. 35, pp. 38260\u201338273 (2022)"},{"key":"6_CR16","unstructured":"Draxler, F., Veschgini, K., Salmhofer, M., Hamprecht, F.: Essentially no barriers in neural network energy landscape. In: International Conference on Machine Learning, pp. 1309\u20131318. PMLR (2018)"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Feng, L., Li, S., Qian, Z., Zhang, X.: Stealthy backdoor attack with adversarial training. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2969\u20132973. IEEE (2022)","DOI":"10.1109\/ICASSP43922.2022.9746008"},{"key":"6_CR18","unstructured":"Foret, P., Kleiner, A., Mobahi, H., Neyshabur, B.: Sharpness-aware minimization for efficiently improving generalization. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=6Tm1mposlrM"},{"key":"6_CR19","unstructured":"Gao, Y., et al.: Backdoor attacks and countermeasures on deep learning: a comprehensive review. arXiv preprint arXiv:2007.10760 (2020)"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Gao, Y., Xu, C., Wang, D., Chen, S., Ranasinghe, D.C., Nepal, S.: STRIP: a defence against trojan attacks on deep neural networks. In: Proceedings of the 35th Annual Computer Security Applications Conference, pp. 113\u2013125 (2019)","DOI":"10.1145\/3359789.3359790"},{"key":"6_CR21","doi-asserted-by":"crossref","unstructured":"Garg, S., Kumar, A., Goel, V., Liang, Y.: Can adversarial weight perturbations inject neural backdoors. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 2029\u20132032 (2020)","DOI":"10.1145\/3340531.3412130"},{"key":"6_CR22","unstructured":"Garipov, T., Izmailov, P., Podoprikhin, D., Vetrov, D.P., Wilson, A.G.: Loss surfaces, mode connectivity, and fast ensembling of DNNs. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"issue":"2","key":"6_CR23","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.1109\/TPAMI.2022.3162397","volume":"45","author":"M Goldblum","year":"2022","unstructured":"Goldblum, M., et al.: Dataset security for machine learning: data poisoning, backdoor attacks, and defenses. IEEE Trans. Pattern Anal. Mach. Intell. 45(2), 1563\u20131580 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"6_CR24","unstructured":"Gu, T., Dolan-Gavitt, B., Garg, S.: BadNets: identifying vulnerabilities in the machine learning model supply chain. arXiv preprint arXiv:1708.06733 (2017)"},{"key":"6_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1007\/978-3-030-58583-9_9","volume-title":"Computer Vision \u2013 ECCV 2020","author":"J Guo","year":"2020","unstructured":"Guo, J., Liu, C.: Practical poisoning attacks on neural networks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12372, pp. 142\u2013158. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58583-9_9"},{"key":"6_CR26","unstructured":"Gurbuz, M.B., Dovrolis, C.: NISPA: Neuro-inspired stability-plasticity adaptation for continual learning in sparse networks. In: Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., Sabato, S. (eds.) Proceedings of the 39th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol.\u00a0162, pp. 8157\u20138174. PMLR (2022). https:\/\/proceedings.mlr.press\/v162\/gurbuz22a.html"},{"key":"6_CR27","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"1","key":"6_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1162\/neco.1997.9.1.1","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Flat minima. Neural Comput. 9(1), 1\u201342 (1997)","journal-title":"Neural Comput."},{"key":"6_CR29","doi-asserted-by":"crossref","unstructured":"Houben, S., Stallkamp, J., Salmen, J., Schlipsing, M., Igel, C.: Detection of traffic signs in real-world images: the German Traffic Sign Detection Benchmark. In: International Joint Conference on Neural Networks (2013)","DOI":"10.1109\/IJCNN.2013.6706807"},{"key":"6_CR30","unstructured":"Hu, X., Lin, X., Cogswell, M., Yao, Y., Jha, S., Chen, C.: Trigger hunting with a topological prior for trojan detection. In: International Conference on Learning Representations (2022). https:\/\/openreview.net\/forum?id=TXsjU8BaibT"},{"key":"6_CR31","unstructured":"Huang, H., Ma, X., Erfani, S.M., Bailey, J.: Distilling cognitive backdoor patterns within an image. In: ICLR (2023)"},{"key":"6_CR32","doi-asserted-by":"crossref","unstructured":"Huang, Z., Chen, J., Zhang, J., Shan, H.: Learning representation for clustering via prototype scattering and positive sampling. IEEE Trans. Pattern Anal. Mach. Intell. (2022)","DOI":"10.1109\/TPAMI.2022.3216454"},{"issue":"13","key":"6_CR33","doi-asserted-by":"publisher","first-page":"3521","DOI":"10.1073\/pnas.1611835114","volume":"114","author":"J Kirkpatrick","year":"2017","unstructured":"Kirkpatrick, J., et al.: Overcoming catastrophic forgetting in neural networks. Proc. Natl. Acad. Sci. 114(13), 3521\u20133526 (2017)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"6_CR34","unstructured":"Krizhevsky, A.: Learning multiple layers of features from tiny images. In: Technical report (2009)"},{"key":"6_CR35","unstructured":"Kumar, R.S.S., et al.: Adversarial machine learning\u2013industry perspectives. arXiv preprint arXiv:2002.05646 (2020)"},{"key":"6_CR36","unstructured":"Kwon, J., Kim, J., Park, H., Choi, I.K.: ASAM: adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks. In: International Conference on Machine Learning, pp. 5905\u20135914. PMLR (2021)"},{"key":"6_CR37","unstructured":"Li, H., Xu, Z., Taylor, G., Studer, C., Goldstein, T.: Advances in neural information processing systems, vol. 31 (2018)"},{"key":"6_CR38","unstructured":"Li, Y., Lyu, X., Koren, N., Lyu, L., Li, B., Ma, X.: Anti-backdoor learning: training clean models on poisoned data. In: Advances in Neural Information Processing Systems, vol. 34, pp. 14900\u201314912 (2021)"},{"key":"6_CR39","unstructured":"Li, Y., Lyu, X., Koren, N., Lyu, L., Li, B., Ma, X.: Neural attention distillation: erasing backdoor triggers from deep neural networks. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=9l0K4OM-oXE"},{"key":"6_CR40","unstructured":"Li, Y., Jiang, Y., Li, Z., Xia, S.T.: Backdoor learning: a survey. IEEE Trans. Neural Netw. Learn. Syst. (2022)"},{"key":"6_CR41","unstructured":"Li, Y., Wu, B., Jiang, Y., Li, Z., Xia, S.T.: Backdoor learning: a survey. arXiv preprint arXiv:2007.08745 (2020)"},{"key":"6_CR42","doi-asserted-by":"crossref","unstructured":"Lin, G., Chu, H., Lai, H.: Towards better plasticity-stability trade-off in incremental learning: a simple linear connector. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 89\u201398 (2022)","DOI":"10.1109\/CVPR52688.2022.00019"},{"key":"6_CR43","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/978-3-030-00470-5_13","volume-title":"Research in Attacks, Intrusions, and Defenses","author":"K Liu","year":"2018","unstructured":"Liu, K., Dolan-Gavitt, B., Garg, S.: Fine-pruning: defending against backdooring attacks on deep neural networks. In: Bailey, M., Holz, T., Stamatogiannakis, M., Ioannidis, S. (eds.) RAID 2018. LNCS, vol. 11050, pp. 273\u2013294. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00470-5_13"},{"key":"6_CR44","doi-asserted-by":"publisher","first-page":"12103","DOI":"10.1109\/ACCESS.2018.2805680","volume":"6","author":"Q Liu","year":"2018","unstructured":"Liu, Q., Li, P., Zhao, W., Cai, W., Yu, S., Leung, V.C.: A survey on security threats and defensive techniques of machine learning: a data driven view. IEEE Access 6, 12103\u201312117 (2018)","journal-title":"IEEE Access"},{"key":"6_CR45","doi-asserted-by":"crossref","unstructured":"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\u20131282 (2019)","DOI":"10.1145\/3319535.3363216"},{"key":"6_CR46","doi-asserted-by":"crossref","unstructured":"Liu, Y., Mai, S., Chen, X., Hsieh, C.J., You, Y.: Towards efficient and scalable sharpness-aware minimization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12360\u201312370 (2022)","DOI":"10.1109\/CVPR52688.2022.01204"},{"key":"6_CR47","unstructured":"Lopez-Paz, D., Ranzato, M.: Gradient episodic memory for continual learning. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"6_CR48","doi-asserted-by":"crossref","unstructured":"Mallya, A., Davis, D., Lazebnik, S.: Piggyback: adapting a single network to multiple tasks by learning to mask weights. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 67\u201382 (2018)","DOI":"10.1007\/978-3-030-01225-0_5"},{"key":"6_CR49","doi-asserted-by":"crossref","unstructured":"Mallya, A., Lazebnik, S.: PackNet: adding multiple tasks to a single network by iterative pruning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7765\u20137773 (2018)","DOI":"10.1109\/CVPR.2018.00810"},{"key":"6_CR50","unstructured":"Mirzadeh, S.I., Farajtabar, M., Gorur, D., Pascanu, R., Ghasemzadeh, H.: Linear mode connectivity in multitask and continual learning. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=Fmg_fQYUejf"},{"key":"6_CR51","unstructured":"Mirzadeh, S.I., Farajtabar, M., Pascanu, R., Ghasemzadeh, H.: Understanding the role of training regimes in continual learning. In: Advances in Neural Information Processing Systems, vol. 33, pp. 7308\u20137320 (2020)"},{"key":"6_CR52","unstructured":"Nguyen, C.V., Li, Y., Bui, T.D., Turner, R.E.: Variational continual learning. In: International Conference on Learning Representations (2018). https:\/\/openreview.net\/forum?id=BkQqq0gRb"},{"key":"6_CR53","doi-asserted-by":"publisher","first-page":"107166","DOI":"10.1016\/j.engappai.2023.107166","volume":"127","author":"TD Nguyen","year":"2024","unstructured":"Nguyen, T.D., Nguyen, T., Le Nguyen, P., Pham, H.H., Doan, K.D., Wong, K.S.: Backdoor attacks and defenses in federated learning: survey, challenges and future research directions. Eng. Appl. Artif. Intell. 127, 107166 (2024)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"6_CR54","unstructured":"Nguyen, T.D., Nguyen, T.A., Tran, A., Doan, K.D., Wong, K.S.: IBA: towards irreversible backdoor attacks in federated learning. In: Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information Processing Systems, vol.\u00a036, pp. 66364\u201366376. Curran Associates, Inc. (2023). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2023\/file\/d0c6bc641a56bebee9d985b937307367-Paper-Conference.pdf"},{"key":"6_CR55","unstructured":"Nguyen, T.A., Tran, A.: Input-aware dynamic backdoor attack. In: Advances in Neural Information Processing Systems, vol. 33, pp. 3454\u20133464 (2020)"},{"key":"6_CR56","unstructured":"Nguyen, T.A., Tran, A.T.: WaNet - imperceptible warping-based backdoor attack. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=eEn8KTtJOx"},{"key":"6_CR57","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1007\/978-3-030-58536-5_31","volume-title":"Computer Vision \u2013 ECCV 2020","author":"A Prabhu","year":"2020","unstructured":"Prabhu, A., Torr, P.H.S., Dokania, P.K.: GDumb: a simple approach that questions our progress in continual learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020, Part II. LNCS, vol. 12347, pp. 524\u2013540. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58536-5_31"},{"key":"6_CR58","unstructured":"Qin, Y., Carlini, N., Cottrell, G., Goodfellow, I., Raffel, C.: Imperceptible, robust, and targeted adversarial examples for automatic speech recognition. In: International Conference on Machine Learning, pp. 5231\u20135240. PMLR (2019)"},{"key":"6_CR59","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"issue":"8","key":"6_CR60","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners. OpenAI blog 1(8), 9 (2019)","journal-title":"OpenAI blog"},{"key":"6_CR61","unstructured":"Ramakrishnan, G., Albarghouthi, A.: Backdoors in neural models of source code. arXiv preprint arXiv:2006.06841 (2020)"},{"key":"6_CR62","doi-asserted-by":"crossref","unstructured":"Ribeiro, M., Grolinger, K., Capretz, M.A.: Mlaas: Machine learning as a service. In: 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), pp. 896\u2013902. IEEE (2015)","DOI":"10.1109\/ICMLA.2015.152"},{"key":"6_CR63","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"issue":"3","key":"6_CR64","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211\u2013252 (2015). https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int. J. Comput. Vision"},{"key":"6_CR65","unstructured":"Shafahi, A., et al.: Poison frogs! Targeted clean-label poisoning attacks on neural networks. In: Advances in Neural Information Processing Systems, pp. 6103\u20136113 (2018)"},{"key":"6_CR66","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"6_CR67","unstructured":"Tran, B., Li, J., Madry, A.: Spectral signatures in backdoor attacks. In: Advances in Neural Information Processing Systems, pp. 8000\u20138010 (2018)"},{"key":"6_CR68","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1007\/978-3-031-05933-9_2","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"LN Van","year":"2022","unstructured":"Van, L.N., Hai, N.L., Pham, H., Than, K.: Auxiliary local variables for improving regularization\/prior approach in continual learning. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds.) PAKDD 2022. LNCS, vol. 13280, pp. 16\u201328. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-05933-9_2"},{"key":"6_CR69","doi-asserted-by":"crossref","unstructured":"Wang, B., et al.: Neural cleanse: identifying and mitigating backdoor attacks in neural networks. In: 2019 IEEE Symposium on Security and Privacy (SP), pp. 707\u2013723. IEEE (2019)","DOI":"10.1109\/SP.2019.00031"},{"key":"6_CR70","unstructured":"Wei, J., et al.: Finetuned language models are zero-shot learners. In: International Conference on Learning Representations (2022). https:\/\/openreview.net\/forum?id=gEZrGCozdqR"},{"key":"6_CR71","unstructured":"Wu, D., Wang, Y.: Adversarial neuron pruning purifies backdoored deep models. In: Advances in Neural Information Processing Systems, vol. 34, pp. 16913\u201316925 (2021)"},{"key":"6_CR72","doi-asserted-by":"crossref","unstructured":"Yan, S., Xie, J., He, X.: DER: dynamically expandable representation for class incremental learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3014\u20133023 (2021)","DOI":"10.1109\/CVPR46437.2021.00303"},{"key":"6_CR73","unstructured":"Yoon, J., Yang, E., Lee, J., Hwang, S.J.: Lifelong learning with dynamically expandable networks. In: International Conference on Learning Representations (2018). https:\/\/openreview.net\/forum?id=Sk7KsfW0-"},{"issue":"9","key":"6_CR74","doi-asserted-by":"publisher","first-page":"2805","DOI":"10.1109\/TNNLS.2018.2886017","volume":"30","author":"X Yuan","year":"2019","unstructured":"Yuan, X., He, P., Zhu, Q., Li, X.: Adversarial examples: attacks and defenses for deep learning. IEEE Trans. Neural Netw. Learn. Syst. 30(9), 2805\u20132824 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"6_CR75","unstructured":"Zenke, F., Poole, B., Ganguli, S.: Continual learning through synaptic intelligence. In: International Conference on Machine Learning, pp. 3987\u20133995. PMLR (2017)"},{"issue":"2","key":"6_CR76","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1049\/cje.2021.00.126","volume":"31","author":"Q Zhang","year":"2022","unstructured":"Zhang, Q., Ma, W., Wang, Y., Zhang, Y., Shi, Z., Li, Y.: Backdoor attacks on image classification models in deep neural networks. Chin. J. Electron. 31(2), 199\u2013212 (2022)","journal-title":"Chin. J. Electron."},{"key":"6_CR77","doi-asserted-by":"crossref","unstructured":"Zhang, X., Xu, R., Yu, H., Zou, H., Cui, P.: Gradient norm aware minimization seeks first-order flatness and improves generalization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20247\u201320257 (2023)","DOI":"10.1109\/CVPR52729.2023.01939"},{"key":"6_CR78","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Liu, Q., Wang, Z., Lu, Z., Hu, Q.: Backdoor defense via deconfounded representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12228\u201312238 (2023)","DOI":"10.1109\/CVPR52729.2023.01177"},{"key":"6_CR79","unstructured":"Zhang, Z., Lyu, L., Wang, W., Sun, L., Sun, X.: How to inject backdoors with better consistency: logit anchoring on clean data. In: International Conference on Learning Representations (2022). https:\/\/openreview.net\/forum?id=Bn09TnDngN"},{"key":"6_CR80","doi-asserted-by":"crossref","unstructured":"Zhu, M., Wei, S., Shen, L., Fan, Y., Wu, B.: Enhancing fine-tuning based backdoor defense with sharpness-aware minimization. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 4466\u20134477 (2023)","DOI":"10.1109\/ICCV51070.2023.00412"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73021-4_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T09:43:09Z","timestamp":1732095789000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73021-4_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,21]]},"ISBN":["9783031730207","9783031730214"],"references-count":80,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73021-4_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,21]]},"assertion":[{"value":"21 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}