{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:51:58Z","timestamp":1743036718357,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031431524"},{"type":"electronic","value":"9783031431531"}],"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\/978-3-031-43153-1_28","type":"book-chapter","created":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T21:01:55Z","timestamp":1693861315000},"page":"332-344","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["BHAC-MRI: Backdoor and Hybrid Attacks on MRI Brain Tumor Classification Using CNN"],"prefix":"10.1007","author":[{"given":"Muhammad","family":"Imran","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hassaan Khaliq","family":"Qureshi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Irene","family":"Amerini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,5]]},"reference":[{"issue":"1","key":"28_CR1","first-page":"125","volume":"5","author":"N Garg","year":"2022","unstructured":"Garg, N., Ashrith, K.S., Parveen, G.S., Sai, K.G., Chintamaneni, A., Hasan, F.: Self-driving car to drive autonomously using image processing and deep learning. Int. J. Res. Eng. Sci. Manage. 5(1), 125\u2013132 (2022)","journal-title":"Int. J. Res. Eng. Sci. Manage."},{"doi-asserted-by":"crossref","unstructured":"Chandana, V.S., Vasavi, S.: Autonomous drones based forest surveillance using Faster R-CNN. In: 2022 International Conference on Electronics and Renewable Systems (ICEARS), pp. 1718\u20131723. IEEE, March 2022","key":"28_CR2","DOI":"10.1109\/ICEARS53579.2022.9752298"},{"key":"28_CR3","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1016\/j.future.2021.09.030","volume":"127","author":"MR Hassan","year":"2022","unstructured":"Hassan, M.R., et al.: Prostate cancer classification from ultrasound and MRI images using deep learning based Explainable Artificial Intelligence. Futur. Gener. Comput. Syst. 127, 462\u2013472 (2022)","journal-title":"Futur. Gener. Comput. Syst."},{"issue":"1","key":"28_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12880-020-00530-y","volume":"21","author":"H Hirano","year":"2021","unstructured":"Hirano, H., Minagi, A., Takemoto, K.: Universal adversarial attacks on deep neural networks for medical image classification. BMC Med. Imaging 21(1), 1\u201313 (2021)","journal-title":"BMC Med. Imaging"},{"issue":"5","key":"28_CR5","doi-asserted-by":"publisher","first-page":"6217","DOI":"10.1007\/s11042-021-11135-0","volume":"81","author":"H Kwon","year":"2022","unstructured":"Kwon, H., Kim, Y.: BlindNet backdoor: attack on deep neural network using blind watermark. Multimedia Tools Appl. 81(5), 6217\u20136234 (2022)","journal-title":"Multimedia Tools Appl."},{"unstructured":"Joel, M.Z., et al.: Adversarial attack vulnerability of deep learning models for oncologic images. medRxiv, January 2021","key":"28_CR6"},{"unstructured":"Yang, C., Wu, Q., Li, H., Chen, Y.: Generative poisoning attack method against neural networks. arXiv preprint arXiv:1703.01340 (2017)","key":"28_CR7"},{"unstructured":"Liao, C., Zhong, H., Squicciarini, A., Zhu, S., Miller, D.: Backdoor embedding in convolutional neural network models via invisible perturbation. arXiv preprint arXiv:1808.10307 (2018)","key":"28_CR8"},{"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":"28_CR9"},{"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, September 2019","key":"28_CR10","DOI":"10.1109\/ICIP.2019.8802997"},{"unstructured":"Turner, A., Tsipras, D., Madry, A.: Clean-label backdoor attacks (2018)","key":"28_CR11"},{"unstructured":"Xiao, H., Xiao, H., Eckert, C.: Adversarial label flips attack on support vector machines. In: ECAI 2012, pp. 870\u2013875. IOS Press (2012)","key":"28_CR12"},{"key":"28_CR13","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.neucom.2014.08.081","volume":"160","author":"H Xiao","year":"2015","unstructured":"Xiao, H., Biggio, B., Nelson, B., Xiao, H., Eckert, C., Roli, F.: Support vector machines under adversarial label contamination. Neurocomputing 160, 53\u201362 (2015)","journal-title":"Neurocomputing"},{"unstructured":"Koh, P.W., Liang, P.: Understanding black-box predictions via influence functions. In: International Conference on Machine Learning, pp. 1885\u20131894. PMLR, July 2017","key":"28_CR14"},{"doi-asserted-by":"crossref","unstructured":"Mei, S., Zhu, X.: Using machine teaching to identify optimal training-set attacks on machine learners. In: Twenty-Ninth AAAI Conference on Artificial Intelligence, February 2015","key":"28_CR15","DOI":"10.1609\/aaai.v29i1.9569"},{"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":"28_CR16"},{"unstructured":"Steinhardt, J., Koh, P.W.W., Liang, P.S.: Certified defenses for data poisoning attacks. In: Advances in Neural Information Processing Systems, vol. 30 (2017)","key":"28_CR17"},{"unstructured":"Nwadike, M., Miyawaki, T., Sarkar, E., Maniatakos, M., Shamout, F.: Explainability matters: backdoor attacks on medical imaging. arXiv preprint arXiv:2101.00008 (2020)","key":"28_CR18"},{"issue":"20","key":"28_CR19","doi-asserted-by":"publisher","first-page":"9556","DOI":"10.3390\/app11209556","volume":"11","author":"Y Matsuo","year":"2021","unstructured":"Matsuo, Y., Takemoto, K.: Backdoor attacks to deep neural network-based system for COVID-19 detection from chest X-ray images. Appl. Sci. 11(20), 9556 (2021)","journal-title":"Appl. Sci."},{"key":"28_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1007\/978-3-030-00928-1_56","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"M Paschali","year":"2018","unstructured":"Paschali, M., Conjeti, S., Navarro, F., Navab, N.: Generalizability vs. Robustness: investigating medical imaging networks using adversarial examples. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 493\u2013501. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_56"},{"doi-asserted-by":"crossref","unstructured":"Feng, Y., Ma, B., Zhang, J., Zhao, S., Xia, Y., Tao, D.: FIBA: frequency-injection based backdoor attack in medical image analysis. arXiv preprint arXiv:2112.01148 (2021)","key":"28_CR21","DOI":"10.1109\/CVPR52688.2022.02021"},{"issue":"3","key":"28_CR22","doi-asserted-by":"publisher","first-page":"1526","DOI":"10.1109\/TSC.2020.3000900","volume":"15","author":"S Wang","year":"2020","unstructured":"Wang, S., Nepal, S., Rudolph, C., Grobler, M., Chen, S., Chen, T.: Backdoor attacks against transfer learning with pre-trained deep learning models. IEEE Trans. Serv. Comput. 15(3), 1526\u20131539 (2020)","journal-title":"IEEE Trans. Serv. Comput."},{"doi-asserted-by":"publisher","unstructured":"Bhuvaji, S., Kadam, A., Bhumkar, P., Dedge, S., Kanchan, S.: Brain Tumor Classification (MRI), [Dataset]. Kaggle (2020). https:\/\/doi.org\/10.34740\/KAGGLE\/DSV\/1183165","key":"28_CR23","DOI":"10.34740\/KAGGLE\/DSV\/1183165"},{"unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)","key":"28_CR24"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing \u2013 ICIAP 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43153-1_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T11:37:07Z","timestamp":1710329827000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43153-1_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031431524","9783031431531"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43153-1_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Udine","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iciap2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"144","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"85","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"59% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"https:\/\/iciap2023.org\/satellite-event\/workshops\/","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}