{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T04:45:01Z","timestamp":1775018701208,"version":"3.50.1"},"reference-count":23,"publisher":"Pleiades Publishing Ltd","issue":"7","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"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":["Program Comput Soft"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1134\/s0361768825700379","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T19:52:00Z","timestamp":1768593120000},"page":"499-509","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Defender\u2019s Dilemma: Are Defense Methods Against Different Attacks on Machine Learning Models Compatible?"],"prefix":"10.1134","volume":"51","author":[{"given":"G. V.","family":"Sazonov","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K. S.","family":"Lukyanov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"I. N.","family":"Meleshin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"137","published-online":{"date-parts":[[2026,1,16]]},"reference":[{"key":"3969_CR1","doi-asserted-by":"publisher","unstructured":"Conti, M., Li, J., Picek, S., and Xu, J., Label-only membership inference attack against node-level graph neural networks, Proceedings of the 15th ACM Workshop on Artificial Intelligence and Security, Los Angeles, 2022, New York: Association for Computing Machinery, 2022, pp. 1\u201312. https:\/\/doi.org\/10.1145\/3560830.3563734","DOI":"10.1145\/3560830.3563734"},{"key":"3969_CR2","unstructured":"Dai, H., Li, H., Tian, T., Huang, X., Wang, L., Zhu, J., and Song, L., Adversarial attack on graph structured data, International Conference on Machine Learning, PMLR, 2018, pp. 1115\u20131124."},{"key":"3969_CR3","doi-asserted-by":"publisher","first-page":"2493","DOI":"10.1109\/tkde.2019.2957786","volume":"33","author":"F. Feng","year":"2019","unstructured":"Feng, F., He, X., Tang, J., and Chua, T.-S., Graph adversarial training: Dynamically regularizing based on graph structure, IEEE Trans. Knowl. Data Eng., 2019, vol. 33, no. 6, pp. 2493\u20132504. https:\/\/doi.org\/10.1109\/tkde.2019.2957786","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"3969_CR4","doi-asserted-by":"publisher","unstructured":"Finlay, C. and Oberman, A.M., Scaleable input gradient regularization for adversarial robustness, arXiv Preprint, 2019. https:\/\/doi.org\/10.48550\/arXiv.1905.11468","DOI":"10.48550\/arXiv.1905.11468"},{"key":"3969_CR5","doi-asserted-by":"publisher","unstructured":"Goodfellow, I.J., Shlens, J., and Szegedy, C., Explaining and harnessing adversarial examples, arXiv Preprint, 2014. https:\/\/doi.org\/10.48550\/arXiv.1412.6572","DOI":"10.48550\/arXiv.1412.6572"},{"key":"3969_CR6","doi-asserted-by":"publisher","unstructured":"Ma, J., Deng, J., and Mei, Q., Adversarial Attack on Graph Neural Networks as an Influence Maximization Problem, Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, ACM, 2022, pp. 675\u2013685. https:\/\/doi.org\/10.1145\/3488560.3498497","DOI":"10.1145\/3488560.3498497"},{"key":"3969_CR7","doi-asserted-by":"publisher","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A., Towards deep learning models resistant to adversarial attacks, arXiv Preprint, 2017. https:\/\/doi.org\/10.48550\/arXiv.1706.06083","DOI":"10.48550\/arXiv.1706.06083"},{"key":"3969_CR8","doi-asserted-by":"publisher","unstructured":"McAuley, J., Targett, Ch., Shi, Q., and Van Den Hengel, A., Image-based recommendations on styles and substitutes, Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago, 2015, New York: Association for Computing Machinery, 2015, pp. 43\u201352. https:\/\/doi.org\/10.1145\/2766462.2767755","DOI":"10.1145\/2766462.2767755"},{"key":"3969_CR9","doi-asserted-by":"publisher","unstructured":"Moosavi-Dezfooli, S.-M., Fawzi, A., and Frossard, P., DeepFool: A simple and accurate method to fool deep neural networks, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 2016, IEEE, 2016, pp. 2574\u20132582. https:\/\/doi.org\/10.1109\/cvpr.2016.282","DOI":"10.1109\/cvpr.2016.282"},{"key":"3969_CR10","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1609\/aimag.v29i3.2157","volume":"29","author":"P. Sen","year":"2008","unstructured":"Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., and Eliassi-rad, T., Collective classification in network data, AI Mag., 2008, vol. 29, no. 3, pp. 93\u2013106. https:\/\/doi.org\/10.1609\/aimag.v29i3.2157","journal-title":"AI Mag."},{"key":"3969_CR11","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/s10958-024-07429-x","volume":"285","author":"D. Shaikhelislamov","year":"2024","unstructured":"Shaikhelislamov, D., Lukyanov, K., Severin, N., Drobyshevskiy, M., Makarov, I., and Turdakov, D., A study of graph neural networks for link prediction on vulnerability to membership attacks, J. Math. Sci., 2024, vol. 285, no. 2, pp. 234\u2013244. https:\/\/doi.org\/10.1007\/s10958-024-07429-x","journal-title":"J. Math. Sci."},{"key":"3969_CR12","unstructured":"Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I.J., and Fergus, R., Intriguing properties of neural networks, 2nd International Conference on Learning Representations, 2014."},{"key":"3969_CR13","doi-asserted-by":"publisher","first-page":"15179","DOI":"10.1609\/aaai.v37i12.26771","volume":"37","author":"S. Szyller","year":"2023","unstructured":"Szyller, S. and Asokan, N., Conflicting interactions among protection mechanisms for machine learning models, Proceedings of the AAAI Conference on Artificial Intelligence, 2023, vol. 37, no. 12, pp. 15179\u201315187. https:\/\/doi.org\/10.1609\/aaai.v37i12.26771","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"3969_CR14","doi-asserted-by":"publisher","first-page":"6262","DOI":"10.1007\/s10489-021-02759-8","volume":"52","author":"Sh. Wang","year":"2022","unstructured":"Wang, Sh. and Gong, Yu., Adversarial example detection based on saliency map features, Appl. Intell., 2022, vol. 52, no. 6, pp. 6262\u20136275. https:\/\/doi.org\/10.1007\/s10489-021-02759-8","journal-title":"Appl. Intell."},{"key":"3969_CR15","doi-asserted-by":"publisher","unstructured":"Wu, H., Wang, Ch., Tyshetskiy, Yu., Docherty, A., Lu, K., and Zhu, L., Adversarial examples for graph data: Deep insights into attack and defense, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence Organization, 2019, pp. 4816\u20134823. https:\/\/doi.org\/10.24963\/ijcai.2019\/669","DOI":"10.24963\/ijcai.2019\/669"},{"key":"3969_CR16","doi-asserted-by":"publisher","first-page":"1258","DOI":"10.1109\/tetci.2022.3147508","volume":"6","author":"X. Yuan","year":"2022","unstructured":"Yuan, X., Ding, L., Zhang, L., Li, X., and Wu, D.O., ES attack: Model stealing against deep neural networks without data hurdles, IEEE Trans. Emerging Top. Comput. Intell., 2022, vol. 6, no. 5, pp. 1258\u20131270. https:\/\/doi.org\/10.1109\/tetci.2022.3147508","journal-title":"IEEE Trans. Emerging Top. Comput. Intell."},{"key":"3969_CR17","doi-asserted-by":"publisher","unstructured":"Zhang, S., Chen, H., Sun, X., Li, Yi., and Xu, G., Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation, Proceedings of the ACM Web Conference 2022, Lyon, 2022, Laforest, F., Troncy, R., Simperl, E., Agarwal, D., Gionis, A., Herman, I., and M\u00e9dini, L., Eds., New York: Association for Computing Machinery, 2022, pp. 1322\u20131330. https:\/\/doi.org\/10.1145\/3485447.3512179","DOI":"10.1145\/3485447.3512179"},{"key":"3969_CR18","first-page":"9263","volume":"33","author":"X. Zhang","year":"2020","unstructured":"Zhang, X. and Zitnik, M., Gnnguard: Defending graph neural networks against adversarial attacks, Advances in Neural Information Processing Systems, 2020, vol. 33, pp. 9263\u20139275.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"3969_CR19","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Jia, J., Wang, B., and Gong, N.Zh., Backdoor attacks to graph neural networks, Proceedings of the 26th ACM Symposium on Access Control Models and Technologies, New York: Association for Computing Machinery, 2021, pp. 15\u201326. https:\/\/doi.org\/10.1145\/3450569.3463560","DOI":"10.1145\/3450569.3463560"},{"key":"3969_CR20","doi-asserted-by":"publisher","unstructured":"Zheng, H., Xiong, H., Chen, J., Ma, H., and Huang, G., Motif-backdoor: Rethinking the backdoor attack on graph neural networks via motifs, arXiv Preprint, 2022. https:\/\/doi.org\/10.48550\/arXiv.2210.13710","DOI":"10.48550\/arXiv.2210.13710"},{"key":"3969_CR21","doi-asserted-by":"publisher","unstructured":"Zhu, D., Zhang, Z., Cui, P., and Zhu, W., Robust graph convolutional networks against adversarial attacks, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, 2019, New York: Association for Computing Machinery, 2019, pp. 1399\u20131407. https:\/\/doi.org\/10.1145\/3292500.3330851","DOI":"10.1145\/3292500.3330851"},{"key":"3969_CR22","doi-asserted-by":"publisher","unstructured":"Z\u00fcgner, D., Akbarnejad, A., and G\u00fcnnemann, S., Adversarial attacks on neural networks for graph data, Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, 2018, New York: Association for Computing Machinery, 2018, pp. 2847\u20132856. https:\/\/doi.org\/10.1145\/3219819.3220078","DOI":"10.1145\/3219819.3220078"},{"key":"3969_CR23","doi-asserted-by":"publisher","unstructured":"Z\u00fcgner, D., Akbarnejad, A., and G\u00fcnnemann, S., Adversarial attacks on neural networks for graph data, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence Organization, 2019. https:\/\/doi.org\/10.24963\/ijcai.2019\/872","DOI":"10.24963\/ijcai.2019\/872"}],"container-title":["Programming and Computer Software"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1134\/S0361768825700379.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1134\/S0361768825700379","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1134\/S0361768825700379.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T02:55:09Z","timestamp":1775012109000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1134\/S0361768825700379"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":23,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["3969"],"URL":"https:\/\/doi.org\/10.1134\/s0361768825700379","relation":{},"ISSN":["0361-7688","1608-3261"],"issn-type":[{"value":"0361-7688","type":"print"},{"value":"1608-3261","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12]]},"assertion":[{"value":"11 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 September 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 September 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors of this work declare that they have no conflicts of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"CONFLICT OF INTEREST"}}]}}