{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:18:59Z","timestamp":1771024739007,"version":"3.50.1"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031200953","type":"print"},{"value":"9783031200960","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-20096-0_20","type":"book-chapter","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T15:04:11Z","timestamp":1673535851000},"page":"257-270","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Explanation-Guided Minimum Adversarial Attack"],"prefix":"10.1007","author":[{"given":"Mingting","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaozhang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anli","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Qi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,13]]},"reference":[{"key":"20_CR1","unstructured":"Molnar, C.: Interpretable Machine Learning (2020). https:\/\/www.lulu.com\/"},{"key":"20_CR2","doi-asserted-by":"crossref","unstructured":"Tu, C.C., Ting, P., Chen, P.Y., et al.: Autozoom: autoencoder-based zeroth order optimization method for attacking black-box neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33(01), pp. 742\u2013749 (2019)","DOI":"10.1609\/aaai.v33i01.3301742"},{"key":"20_CR3","unstructured":"A\u00efvodji, U., Bolot, A., Gambs, S.: Model extraction from counterfactual explanations. arXiv preprint arXiv:2009.01884 (2020)"},{"key":"20_CR4","doi-asserted-by":"crossref","unstructured":"Amich, A., Eshete, B.: EG-Booster: explanation-guided booster of ML evasion attacks. arXiv preprint arXiv:2108.13930 (2021)","DOI":"10.1145\/3508398.3511510"},{"key":"20_CR5","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). IEEE, pp. 39\u201357 (2017)","DOI":"10.1109\/SP.2017.49"},{"issue":"1","key":"20_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-019-0874-0","volume":"19","author":"R Elshawi","year":"2019","unstructured":"Elshawi, R., Al-Mallah, M.H., Sakr, S.: On the interpretability of machine learning-based model for predicting hypertension. BMC Med. Inform. Decis. Making 19(1), 1\u201332 (2019)","journal-title":"BMC Med. Inform. Decis. Making"},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Shokri, R., Strobel, M., Zick, Y.: On the privacy risks of model explanations. In: AIES 2021: AAAI\/ACM Conference on AI, Ethics, and Society. ACM (2021)","DOI":"10.1145\/3461702.3462533"},{"key":"20_CR8","unstructured":"Garcia, W., Choi, J.I., Adari, S.K., et al.: Explainable black-box attacks against model-based authentication. arXiv preprint arXiv:1810.00024 (2018)"},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.M., Fawzi, A., Frossard, P.: DeepFool: a simple and accurate method to fool deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2574\u20132582 (2016)","DOI":"10.1109\/CVPR.2016.282"},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Milli, S., Schmidt, L., Dragan, A.D., et al.: Model reconstruction from model explanations. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 1\u20139 (2019)","DOI":"10.1145\/3287560.3287562"},{"key":"20_CR11","unstructured":"Ovadia, Y., Fertig, E., et al.: Can you trust your model\u2019s uncertainty? evaluating predictive uncertainty under dataset shift (2019)"},{"key":"20_CR12","doi-asserted-by":"crossref","unstructured":"Papernot, N., McDaniel, P., Jha, S., et al.: The limitations of deep learning in adversarial settings. In: 2016 IEEE European Symposium on Security and Privacy (EuroS &P), pp. 372\u2013387 IEEE (2016)","DOI":"10.1109\/EuroSP.2016.36"},{"key":"20_CR13","doi-asserted-by":"crossref","unstructured":"Ribeiro, MT., Singh, S., Guestrin, C.: Why should I trust you?: explaining the predictions of any classifier. In: The 22nd ACM SIGKDD International Conference. ACM (2016)","DOI":"10.1145\/2939672.2939778"},{"issue":"5","key":"20_CR14","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1109\/TEVC.2019.2890858","volume":"23","author":"J Su","year":"2019","unstructured":"Su, J., Vargas, D.V., Sakurai, K.: One pixel attack for fooling deep neural net-works. IEEE Trans. Evol. Comput. 23(5), 828\u2013841 (2019)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"20_CR15","unstructured":"Severi, G., Meyer, J., Coull, S., et al.: Explanation-guided backdoor poisoning attacks against malware classifiers. In: 30th USENIX Security Symposium (USENIX Security 21), pp. 1487\u20131504 (2021)"},{"key":"20_CR16","doi-asserted-by":"crossref","unstructured":"Zhao, X., Zhang, W., Xiao, X., et al.: Exploiting explanations for model inversion attacks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 682\u2013692 (2021)","DOI":"10.1109\/ICCV48922.2021.00072"},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"Chen, P.Y., et al.: Zoo: zeroth order optimization based black-box attacks to deep neural networks without training substitute models. In: Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, pp. 15\u201326 (2017)","DOI":"10.1145\/3128572.3140448"},{"key":"20_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1007\/978-3-030-58592-1_29","volume-title":"Computer Vision \u2013 ECCV 2020","author":"M Andriushchenko","year":"2020","unstructured":"Andriushchenko, M., Croce, F., Flammarion, N., Hein, M.: Square attack: a query-efficient black-box adversarial attack via random search. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12368, pp. 484\u2013501. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58592-1_29"},{"issue":"3","key":"20_CR19","doi-asserted-by":"publisher","first-page":"396","DOI":"10.3390\/e24030396","volume":"24","author":"Z Du","year":"2022","unstructured":"Du, Z., Liu, F., Yan, X.: Minimum adversarial examples. Entropy 24(3), 396 (2022)","journal-title":"Entropy"},{"issue":"2","key":"20_CR20","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","volume":"128","author":"RR Selvaraju","year":"2020","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., et al.: Grad-CAM: visual explanations from deep networks via gradient-based localization. Int. J. Comput. Vis. 128(2), 336\u2013359 (2020)","journal-title":"Int. J. Comput. Vis."},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, Z., Du, M., et al.: Score-CAM: score-weighted visual explanations for convolutional neural networks (2019)","DOI":"10.1109\/CVPRW50498.2020.00020"},{"key":"20_CR22","doi-asserted-by":"crossref","unstructured":"Mothilal, R.K., Sharma, A., Tan, C.: Explaining machine learning classifiers through diverse counterfactual explanations. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 607\u2013617 (2020)","DOI":"10.1145\/3351095.3372850"},{"key":"20_CR23","unstructured":"Ilyas, A., Engstrom, L., Athalye, A., et al.: Query-efficient black-box adversarial examples (superceded). arXiv preprint arXiv:1712.07113 (2017)"},{"key":"20_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-030-33850-3_3","volume-title":"Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support","author":"H Lee","year":"2019","unstructured":"Lee, H., Kim, S.T., Ro, Y.M.: Generation of multimodal justification using visual word constraint model for explainable computer-aided diagnosis. In: Suzuki, K., et al. (eds.) ML-CDS\/IMIMIC -2019. LNCS, vol. 11797, pp. 21\u201329. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-33850-3_3"},{"key":"20_CR25","unstructured":"Meyes, R., de Puiseau, C.W., Posada-Moreno, A., Meisen, T.: Under the hood of neural networks: characterizing learned representations by functional neuron populations and network ablations. arXiv preprint arXiv:2004.01254 (2020)"},{"key":"20_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/978-3-030-02628-8_13","volume-title":"Understanding and Interpreting Machine Learning in Medical Image Computing Applications","author":"P Van Molle","year":"2018","unstructured":"Van Molle, P., De Strooper, M., Verbelen, T., Vankeirsbilck, B., Simoens, P., Dhoedt, B.: Visualizing convolutional neural networks to improve decision support for skin lesion classification. In: Stoyanov, D., et al. (eds.) MLCN\/DLF\/IMIMIC -2018. LNCS, vol. 11038, pp. 115\u2013123. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-02628-8_13"}],"container-title":["Lecture Notes in Computer Science","Machine Learning for Cyber Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20096-0_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T15:09:43Z","timestamp":1673536183000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20096-0_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031200953","9783031200960"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20096-0_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"13 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ML4CS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning for Cyber Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ml4cs2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/ml4cs2022\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}