{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T08:25:27Z","timestamp":1743063927551,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030930455"},{"type":"electronic","value":"9783030930462"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-93046-2_13","type":"book-chapter","created":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T05:30:01Z","timestamp":1641015001000},"page":"149-157","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["White-Box Attacks on the CNN-Based Myoelectric Control System"],"prefix":"10.1007","author":[{"given":"Bo","family":"Xue","sequence":"first","affiliation":[]},{"given":"Le","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Aiping","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Xu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xun","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,1]]},"reference":[{"issue":"4","key":"13_CR1","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.bspc.2007.07.009","volume":"2","author":"MA Oskoei","year":"2007","unstructured":"Oskoei, M.A., Hu, H.: Myoelectric control systems-a survey. Biomed. Signal Process. Control 2(4), 275\u2013294 (2007)","journal-title":"Biomed. Signal Process. Control"},{"issue":"4","key":"13_CR2","doi-asserted-by":"publisher","first-page":"994","DOI":"10.1109\/JBHI.2016.2560907","volume":"21","author":"X Yang","year":"2016","unstructured":"Yang, X., Chen, X., Cao, X., Wei, S., Zhang, X.: Chinese sign language recognition based on an optimized tree-structure framework. IEEE J. Biomed. Health Inform. 21(4), 994\u20131004 (2016)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Park, K.H., Lee, S.W.: Movement intention decoding based on deep learning for multiuser myoelectric interfaces. In: 2016 4th international winter conference on brain-computer Interface (BCI), pp. 1\u20132. IEEE (2016)","DOI":"10.1109\/IWW-BCI.2016.7457459"},{"issue":"5","key":"13_CR4","doi-asserted-by":"publisher","first-page":"814","DOI":"10.1109\/TNSRE.2019.2908955","volume":"27","author":"X Zhang","year":"2019","unstructured":"Zhang, X., Wu, D.: On the vulnerability of CNN classifiers in EEG-based BCIS. IEEE Trans. Neural Syst. Rehabil. Eng. 27(5), 814\u2013825 (2019)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"1","key":"13_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-016-0001-8","volume":"6","author":"W Geng","year":"2016","unstructured":"Geng, W., Du, Y., Jin, W., Wei, W., Hu, Y., Li, J.: Gesture recognition by instantaneous surface EMG images. Sci. Rep. 6(1), 1\u20138 (2016)","journal-title":"Sci. Rep."},{"issue":"6","key":"13_CR6","doi-asserted-by":"publisher","first-page":"1865","DOI":"10.3390\/su10061865","volume":"10","author":"Z Ding","year":"2018","unstructured":"Ding, Z., Yang, C., Tian, Z., Yi, C., Fu, Y., Jiang, F.: SEMG-based gesture recognition with convolution neural networks. Sustainability 10(6), 1865 (2018)","journal-title":"Sustainability"},{"key":"13_CR7","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)"},{"key":"13_CR8","unstructured":"Kurakin, A., Goodfellow, I., Bengio, S., et al.: Adversarial examples in the physical world (2016)"},{"key":"13_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":"13_CR10","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":"13_CR11","doi-asserted-by":"crossref","unstructured":"Baluja, S., Fischer, I.: Adversarial transformation networks: learning to generate adversarial examples. arXiv preprint arXiv:1703.09387 (2017)","DOI":"10.1609\/aaai.v32i1.11672"},{"key":"13_CR12","unstructured":"Liu, Z., Zhang, X., Meng, L., Wu, D.: Universal adversarial perturbations for CNN classifiers in EEG-based BCIS. arXiv preprint arXiv:1912.01171 (2019)"},{"issue":"7","key":"13_CR13","first-page":"1947","volume":"67","author":"X Zhang","year":"2019","unstructured":"Zhang, X., Wu, L., Yu, B., Chen, X., Chen, X.: Adaptive calibration of electrode array shifts enables robust myoelectric control. IEEE Trans. Biomed. Eng. 67(7), 1947\u20131957 (2019)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"11","key":"13_CR14","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"issue":"4","key":"13_CR15","doi-asserted-by":"publisher","first-page":"1292","DOI":"10.1109\/JBHI.2020.3009383","volume":"25","author":"X Chen","year":"2020","unstructured":"Chen, X., Li, Y., Hu, R., Zhang, X., Chen, X.: Hand gesture recognition based on surface electromyography using convolutional neural network with transfer learning method. IEEE J. Biomed. Health Inform. 25(4), 1292\u20131304 (2020)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"13_CR16","doi-asserted-by":"publisher","first-page":"9","DOI":"10.3389\/fnbot.2016.00009","volume":"10","author":"M Atzori","year":"2016","unstructured":"Atzori, M., Cognolato, M., M\u00fcller, H.: Deep learning with convolutional neural networks applied to electromyography data: a resource for the classification of movements for prosthetic hands. Front. Neurorobot. 10, 9 (2016)","journal-title":"Front. Neurorobot."},{"issue":"2","key":"13_CR17","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1109\/TBCAS.2019.2955641","volume":"14","author":"S Tam","year":"2019","unstructured":"Tam, S., Boukadoum, M., Campeau-Lecours, A., Gosselin, B.: A fully embedded adaptive real-time hand gesture classifier leveraging HD-SEMG and deep learning. IEEE Trans. Biomed. Circuits Syst. 14(2), 232\u2013243 (2019)","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1765\u20131773 (2017)","DOI":"10.1109\/CVPR.2017.17"},{"key":"13_CR19","unstructured":"Athalye, A., Carlini, N., Wagner, D.: Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples. In: International Conference on Machine Learning, pp. 274\u2013283. PMLR (2018)"},{"key":"13_CR20","unstructured":"Chen, J., Meng, Z., Sun, C., Tang, W., Zhu, Y.: ReabsNet: detecting and revising adversarial examples. arXiv preprint arXiv:1712.08250 (2017)"},{"key":"13_CR21","unstructured":"Abbasi, M., Gagn\u00e9, C.: Robustness to adversarial examples through an ensemble of specialists. arXiv preprint arXiv:1702.06856 (2017)"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-93046-2_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,21]],"date-time":"2023-01-21T11:05:44Z","timestamp":1674299144000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-93046-2_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030930455","9783030930462"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-93046-2_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CAAI International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hangzhou","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cicai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cicai.caai.cn\/#\/","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":"307","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":"105","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":"0","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":"34% - 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":"3.2","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":"5.3","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)"}}]}}