{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T05:38:20Z","timestamp":1764049100645,"version":"3.45.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T00:00:00Z","timestamp":1764028800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T00:00:00Z","timestamp":1764028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. U24B20172, No. 62571539"],"award-info":[{"award-number":["No. U24B20172, No. 62571539"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-08050-8","type":"journal-article","created":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T04:15:03Z","timestamp":1764044103000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A feature-aligned backdoor attack method for class-incremental learning-based automated modulation recognition"],"prefix":"10.1007","volume":"81","author":[{"given":"Xiangjun","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianglin","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhua","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianfeng","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,25]]},"reference":[{"issue":"3","key":"8050_CR1","doi-asserted-by":"publisher","first-page":"552","DOI":"10.3390\/electronics12030552","volume":"12","author":"H Han","year":"2023","unstructured":"Han H, Yi Z, Zhu Z, Li L, Gong S, Li B, Wang M (2023) Automatic modulation recognition based on deep-learning features fusion of signal and constellation diagram. Electronics 12(3):552","journal-title":"Electronics"},{"key":"8050_CR2","doi-asserted-by":"crossref","unstructured":"Zhao B, Cui Q, Song R, Qiu Y, Liang J (2022) Decoupled knowledge distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 11953\u201311962","DOI":"10.1109\/CVPR52688.2022.01165"},{"issue":"2","key":"8050_CR3","doi-asserted-by":"publisher","first-page":"881","DOI":"10.1109\/TGCN.2022.3186898","volume":"7","author":"P Qi","year":"2022","unstructured":"Qi P, Zhou X, Ding Y, Zheng S, Jiang T, Li Z (2022) Collaborative and incremental learning for modulation classification with heterogeneous local dataset in cognitive IoT. IEEE Trans Green Commun Netw 7(2):881\u2013893","journal-title":"IEEE Trans Green Commun Netw"},{"key":"8050_CR4","doi-asserted-by":"crossref","unstructured":"Yu L, Twardowski B, Liu X, Herranz L, Wang K, Cheng Y, Jui S, Weijer Jvd (2020) Semantic drift compensation for class-incremental learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 6982\u20136991","DOI":"10.1109\/CVPR42600.2020.00701"},{"key":"8050_CR5","doi-asserted-by":"crossref","unstructured":"Van De\u00a0Ven GM, Li Z, Tolias AS (2021) Class-incremental learning with generative classifiers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 3611\u20133620","DOI":"10.1109\/CVPRW53098.2021.00400"},{"key":"8050_CR6","doi-asserted-by":"publisher","first-page":"47230","DOI":"10.1109\/ACCESS.2019.2909068","volume":"7","author":"T Gu","year":"2019","unstructured":"Gu T, Liu K, Dolan-Gavitt B, Garg S (2019) Badnets: Evaluating backdooring attacks on deep neural networks. IEEE Access 7:47230\u201347244","journal-title":"IEEE Access"},{"issue":"2","key":"8050_CR7","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1109\/TDSC.2022.3201234","volume":"21","author":"W Jiang","year":"2022","unstructured":"Jiang W, Zhang T, Qiu H, Li H, Xu G (2022) Incremental learning, incremental backdoor threats. IEEE Trans Dependable Secure Comput 21(2):559\u2013572","journal-title":"IEEE Trans Dependable Secure Comput"},{"key":"8050_CR8","unstructured":"Zhong Y, Liu X, Zhai D, Jiang J, Ji X (2023) Backdoor attacks against incremental learners: an empirical evaluation study. arXiv preprint arXiv:2305.18384"},{"key":"8050_CR9","doi-asserted-by":"crossref","unstructured":"Perla NK, Hossain MI, Sajeeda A, Shao M (2025) Are exemplar-based class incremental learning models victim of black-box poison attacks? In: 2025 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, pp 6785\u20136794","DOI":"10.1109\/WACV61041.2025.00660"},{"key":"8050_CR10","unstructured":"Guo Z, Kumar A, Tourani R (2025) Persistent backdoor attacks in continual learning. In: 34th USENIX Security Symposium (USENIX Security 25), pp 6379\u20136397"},{"issue":"11","key":"8050_CR11","first-page":"4003","volume":"45","author":"X Wang","year":"2023","unstructured":"Wang X, Song C, Yang Z (2023) Classification method for chirp spread spectrum communication formats based on multi-feature fusion. J Electron Inf Technol 45(11):4003\u20134015","journal-title":"J Electron Inf Technol"},{"key":"8050_CR12","doi-asserted-by":"crossref","unstructured":"Feng Y, Duan R, Li S, Cheng P, Liu W (2025) A dual-branch network with feature assistance for automatic modulation recognition. IEEE Signal Process Lett","DOI":"10.1109\/LSP.2025.3527901"},{"issue":"3","key":"8050_CR13","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/s10489-024-06202-6","volume":"55","author":"Z Yi","year":"2025","unstructured":"Yi Z, Meng H, Gao L, He Z, Yang M (2025) Efficient convolutional dual-attention transformer for automatic modulation recognition. Appl Intell 55(3):231","journal-title":"Appl Intell"},{"key":"8050_CR14","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.aej.2024.06.008","volume":"104","author":"R Cheng","year":"2024","unstructured":"Cheng R, Chen Q, Huang M (2024) Automatic modulation recognition using deep cvcnn-lstm architecture. Alex Eng J 104:162\u2013170","journal-title":"Alex Eng J"},{"key":"8050_CR15","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1016\/j.dt.2023.12.013","volume":"34","author":"Y Liu","year":"2024","unstructured":"Liu Y, Yan X, Liu Q, An T, Dai J (2024) Automatic modulation recognition of radio fuzes using a dr2d-based adaptive denoising method and textural feature extraction. Defence Technol 34:328\u2013338","journal-title":"Defence Technol"},{"issue":"1","key":"8050_CR16","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1109\/LCOMM.2023.3331265","volume":"28","author":"C Zhao","year":"2023","unstructured":"Zhao C, Chen J, Huang X, Wu Z (2023) A cross-scale embedding based fusion transformer for automatic modulation recognition. IEEE Commun Lett 28(1):68\u201372","journal-title":"IEEE Commun Lett"},{"key":"8050_CR17","doi-asserted-by":"crossref","unstructured":"Shaik S, Kirthiga S (2021) Automatic modulation classification using densenet. In: 2021 5th International Conference on Computer, Communication and Signal Processing (ICCCSP). IEEE, pp 301\u2013305","DOI":"10.1109\/ICCCSP52374.2021.9465520"},{"issue":"10","key":"8050_CR18","doi-asserted-by":"publisher","first-page":"3287","DOI":"10.1109\/LCOMM.2021.3102656","volume":"25","author":"F Zhang","year":"2021","unstructured":"Zhang F, Luo C, Xu J, Luo Y (2021) An efficient deep learning model for automatic modulation recognition based on parameter estimation and transformation. IEEE Commun Lett 25(10):3287\u20133290","journal-title":"IEEE Commun Lett"},{"issue":"1","key":"8050_CR19","first-page":"174","volume":"15","author":"R Egala","year":"2025","unstructured":"Egala R, Sairam M, Anusha J (2025) Improving cognitive radio network performance using alexnet. Int J Curr Sci (IJCSPUB) 15(1):174\u2013181","journal-title":"Int J Curr Sci (IJCSPUB)"},{"key":"8050_CR20","doi-asserted-by":"crossref","unstructured":"Xu S, Zhou Y, Hu, P (2024) Communication signal automatic modulation classification based on feature fusion. In: 2024 Photonics & Electromagnetics Research Symposium (PIERS). IEEE, pp 1\u20138","DOI":"10.1109\/PIERS62282.2024.10618466"},{"key":"8050_CR21","unstructured":"Xu J, Lin Z (2022) Modulation and classification of mixed signals based on deep learning. arXiv preprint arXiv:2205.09916"},{"key":"8050_CR22","doi-asserted-by":"crossref","unstructured":"Tian X, Chen C (2019) Modulation pattern recognition based on resnet50 neural network. In: 2019 IEEE 2nd International Conference on Information Communication and Signal Processing (ICICSP). IEEE, pp 34\u201338","DOI":"10.1109\/ICICSP48821.2019.8958555"},{"key":"8050_CR23","doi-asserted-by":"crossref","unstructured":"Rebuffi S-A, Kolesnikov A, Sperl G, Lampert CH (2017) icarl: Incremental classifier and representation learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2001\u20132010","DOI":"10.1109\/CVPR.2017.587"},{"key":"8050_CR24","doi-asserted-by":"crossref","unstructured":"Hou S, Pan X, Loy CC, Wang Z, Lin D (2019) Learning a unified classifier incrementally via rebalancing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 831\u2013839","DOI":"10.1109\/CVPR.2019.00092"},{"key":"8050_CR25","doi-asserted-by":"crossref","unstructured":"Castro FM, Mar\u00edn-Jim\u00e9nez MJ, Guil N, Schmid C, Alahari K (2018) End-to-end incremental learning. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 233\u2013248","DOI":"10.1007\/978-3-030-01258-8_15"},{"key":"8050_CR26","unstructured":"Hayes TL, Kanan C. Lifelong machine learning with deep streaming linear discriminant analysis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp 220\u2013221"},{"key":"8050_CR27","doi-asserted-by":"crossref","unstructured":"Liu Y, Schiele B, Sun Q (2021) Adaptive aggregation networks for class-incremental learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 2544\u20132553","DOI":"10.1109\/CVPR46437.2021.00257"},{"key":"8050_CR28","doi-asserted-by":"crossref","unstructured":"Zhu F, Zhang X-Y, Wang C, Yin F, Liu C-L (2021) Prototype augmentation and self-supervision for incremental learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 5871\u20135880","DOI":"10.1109\/CVPR46437.2021.00581"},{"key":"8050_CR29","doi-asserted-by":"crossref","unstructured":"Zhu K, Zhai W, Cao Y, Luo J, Zha Z-J (2022) Self-sustaining representation expansion for non-exemplar class-incremental learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 9296\u20139305","DOI":"10.1109\/CVPR52688.2022.00908"},{"key":"8050_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110943","volume":"157","author":"W Liu","year":"2025","unstructured":"Liu W, Wu X-J, Zhu F, Yu M-M, Wang C, Liu C-L (2025) Class incremental learning with self-supervised pre-training and prototype learning. Pattern Recogn 157:110943","journal-title":"Pattern Recogn"},{"key":"8050_CR31","doi-asserted-by":"crossref","unstructured":"Xu T, Li Y, Jiang Y, Xia S-T (2023) Batt: Backdoor attack with transformation-based triggers. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp 1\u20135","DOI":"10.1109\/ICASSP49357.2023.10096034"},{"key":"8050_CR32","doi-asserted-by":"crossref","unstructured":"Hua Y, Liu L, Cao J, Chen H (2024) Stealth backdoor attack for remote sensing image classification. In: 2024 IEEE 7th International Conference on Electronic Information and Communication Technology (ICEICT). IEEE, pp 229\u2013233","DOI":"10.1109\/ICEICT61637.2024.10671242"},{"key":"8050_CR33","doi-asserted-by":"crossref","unstructured":"Wu D, Hao L, Wei B, Hao K, Han T, He L (2024) Backdoor attack based on privacy inference against federated learning. In: 2024 7th International Symposium on Autonomous Systems (ISAS). IEEE, pp 1\u20136","DOI":"10.1109\/ISAS61044.2024.10552567"},{"key":"8050_CR34","doi-asserted-by":"crossref","unstructured":"Yang C, Zhao H (2024) A dynamic optimization scheme based backdoor attack dpba in federated learning. In: 2024 7th International Conference on Computer Information Science and Application Technology (CISAT). IEEE, pp 1323\u20131327","DOI":"10.1109\/CISAT62382.2024.10695409"},{"key":"8050_CR35","doi-asserted-by":"crossref","unstructured":"Lyu X, Han Y, Wang W, Liu J, Wang B, Chen K, Li Y, Liu J, Zhang X (2024) Coba: Collusive backdoor attacks with optimized trigger to federated learning. IEEE Trans Dependable Secure Comput","DOI":"10.1109\/TDSC.2024.3445637"},{"key":"8050_CR36","doi-asserted-by":"crossref","unstructured":"Qiao Y, Liu D, Wang R, Liang K (2024) Stealthy backdoor attack against federated learning through frequency domain by backdoor neuron constraint and model camouflage. IEEE J Emerging Sel Topics Circuits Syst","DOI":"10.1109\/JETCAS.2024.3450527"},{"key":"8050_CR37","doi-asserted-by":"publisher","first-page":"1057","DOI":"10.1109\/LSP.2023.3293429","volume":"30","author":"Z Ye","year":"2023","unstructured":"Ye Z, Yan D, Dong L, Deng J, Yu S (2023) Stealthy backdoor attack against speaker recognition using phase-injection hidden trigger. IEEE Signal Process Lett 30:1057\u20131061","journal-title":"IEEE Signal Process Lett"},{"key":"8050_CR38","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1109\/TIFS.2023.3322659","volume":"19","author":"W Sun","year":"2023","unstructured":"Sun W, Jiang X, Dou S, Li D, Miao D, Deng C, Zhao C (2023) Invisible backdoor attack with dynamic triggers against person re-identification. IEEE Trans Inf Forensics Secur 19:307\u2013319","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"8050_CR39","doi-asserted-by":"publisher","first-page":"47230","DOI":"10.1109\/ACCESS.2019.2909068","volume":"7","author":"T Gu","year":"2019","unstructured":"Gu T, Liu K, Dolan-Gavitt B, Garg S (2019) Badnets: Evaluating backdooring attacks on deep neural networks. IEEE Access 7:47230\u201347244","journal-title":"IEEE Access"},{"key":"8050_CR40","doi-asserted-by":"crossref","unstructured":"Li Y, Li Y, Wu B, Li L, He R, Lyu S (2021) Invisible backdoor attack with sample-specific triggers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 16463\u201316472","DOI":"10.1109\/ICCV48922.2021.01615"},{"key":"8050_CR41","unstructured":"Nguyen T, Tran A, Ho N (2024) Backdoor attack in prompt-based continual learning. arXiv e-prints, 2406"},{"key":"8050_CR42","unstructured":"Umer M, Polikar R (2022) False memory formation in continual learners through imperceptible backdoor trigger. arXiv preprint arXiv:2202.04479"},{"key":"8050_CR43","doi-asserted-by":"crossref","unstructured":"Umer M, Polikar R (2021) Adversarial targeted forgetting in regularization and generative based continual learning models. In: 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN52387.2021.9533400"},{"key":"8050_CR44","unstructured":"Chen B, Carvalho W, Baracaldo N, Ludwig H, Edwards B, Lee T, Molloy I, Srivastava B (2018) Detecting backdoor attacks on deep neural networks by activation clustering. arXiv preprint arXiv:1811.03728"},{"key":"8050_CR45","unstructured":"Tran B, Li J, Madry A (2018) Spectral signatures in backdoor attacks. In: Advances in neural information processing systems, vol 31"},{"key":"8050_CR46","doi-asserted-by":"crossref","unstructured":"Liu K, Dolan-Gavitt B, Garg S (2018) Fine-pruning: Defending against backdooring attacks on deep neural networks. In: International Symposium on Research in Attacks, Intrusions, and Defenses. Springer, Berlin, pp 273\u2013294","DOI":"10.1007\/978-3-030-00470-5_13"},{"key":"8050_CR47","doi-asserted-by":"crossref","unstructured":"Liu Y, Fan M, Chen C, Liu X, Ma Z, Wang L, Ma J (2022) Backdoor defense with machine unlearning. In: IEEE INFOCOM 2022-IEEE Conference on Computer Communications. IEEE, pp 280\u2013289","DOI":"10.1109\/INFOCOM48880.2022.9796974"},{"key":"8050_CR48","unstructured":"Wu Z, Wen J, Peng W, Zhou Y, Xue Y et al (2025) Bedkd: Backdoor defense based on dynamic knowledge distillation and directional mapping modulator. arXiv preprint arXiv:2508.01595"},{"key":"8050_CR49","doi-asserted-by":"crossref","unstructured":"Gao Y, Xu C, Wang D, Chen S, Ranasinghe DC, Nepal S (2019) Strip: a defence against trojan attacks on deep neural networks. In: Proceedings of the 35th Annual Computer Security Applications Conference, pp 113\u2013125","DOI":"10.1145\/3359789.3359790"},{"issue":"1","key":"8050_CR50","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1109\/JSTSP.2018.2797022","volume":"12","author":"TJ O\u2019Shea","year":"2018","unstructured":"O\u2019Shea TJ, Roy T, Clancy TC (2018) Over-the-air deep learning based radio signal classification. IEEE J Sel Top Signal Process 12(1):168\u2013179","journal-title":"IEEE J Sel Top Signal Process"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-08050-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-08050-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-08050-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T04:15:14Z","timestamp":1764044114000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-08050-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,25]]},"references-count":50,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["8050"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-08050-8","relation":{},"ISSN":["1573-0484"],"issn-type":[{"type":"electronic","value":"1573-0484"}],"subject":[],"published":{"date-parts":[[2025,11,25]]},"assertion":[{"value":"28 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"1594"}}