{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T04:10:25Z","timestamp":1743135025469,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":33,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819996131"},{"type":"electronic","value":"9789819996148"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-9614-8_2","type":"book-chapter","created":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T15:02:31Z","timestamp":1704294151000},"page":"20-37","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Efficient Universal Adversarial Attack on\u00a0Audio Classification Models: A\u00a0Two-Step Method"],"prefix":"10.1007","author":[{"given":"Huifeng","family":"Li","sequence":"first","affiliation":[]},{"given":"Pengzhou","family":"Jia","sequence":"additional","affiliation":[]},{"given":"Weixun","family":"Li","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[]},{"given":"Dexin","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Haoran","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,4]]},"reference":[{"key":"2_CR1","unstructured":"Abdoli, S., Hafemann, L.G., Rony, J., Ayed, I.B., Cardinal, P., Koerich, A.L.: Universal adversarial audio perturbations. arXiv abs\/1908.03173 (2019)"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Alom, M.Z., Aspiras, T.H., Taha, T.M., Asari, V.K.: Skin cancer segmentation and classification with NABLA-N and inception recurrent residual convolutional networks. arXiv abs\/1904.11126 (2019)","DOI":"10.1109\/NAECON.2018.8556737"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Ant\u00f3n, S.D., Kanoor, S., Fraunholz, D., Schotten, H.D.: Evaluation of machine learning-based anomaly detection algorithms on an industrial Modbus\/TCP data set. In: ARES (2018)","DOI":"10.1145\/3230833.3232818"},{"key":"2_CR4","unstructured":"Brown, T.B., Man\u00e9, D., Roy, A., Abadi, M., Gilmer, J.: Adversarial patch. arXiv abs\/1712.09665 (2017)"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Carlini, N., Wagner, D.A.: Towards evaluating the robustness of neural networks. In: 2017 IEEE S &P (2017)","DOI":"10.1109\/SP.2017.49"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Carlini, N., Wagner, D.A.: Audio adversarial examples: targeted attacks on speech-to-text. In: IEEE S &P (2018)","DOI":"10.1109\/SPW.2018.00009"},{"issue":"5","key":"2_CR7","doi-asserted-by":"publisher","first-page":"3970","DOI":"10.1109\/TDSC.2022.3220673","volume":"20","author":"G Chen","year":"2023","unstructured":"Chen, G., et al.: Towards understanding and mitigating audio adversarial examples for speaker recognition. IEEE Trans. Dependable Secure Comput. 20(5), 3970\u20133987 (2023)","journal-title":"IEEE Trans. Dependable Secure Comput."},{"key":"2_CR8","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.neucom.2020.09.052","volume":"422","author":"J Dai","year":"2021","unstructured":"Dai, J., Shu, L.: Fast-UAP: an algorithm for expediting universal adversarial perturbation generation using the orientations of perturbation vectors. Neurocomputing 422, 109\u2013117 (2021)","journal-title":"Neurocomputing"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Dong, Y., et al.: Boosting adversarial attacks with momentum. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00957"},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Durand, T., Mehrasa, N., Mori, G.: Learning a deep convnet for multi-label classification with partial labels. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00074"},{"key":"2_CR11","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: ICLR (2015)"},{"key":"2_CR12","unstructured":"Grosse, K., Trost, T.A., Mosbach, M., Backes, M., Klakow, D.: Adversarial initialization - when your network performs the way I want. arXiv abs\/1902.03020 (2019)"},{"key":"2_CR13","unstructured":"Hannun, A.Y., et al.: Deep speech: scaling up end-to-end speech recognition. arXiv (2014)"},{"key":"2_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/978-3-319-46493-0_38","volume-title":"Computer Vision \u2013 ECCV 2016","author":"K He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630\u2013645. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_38"},{"key":"2_CR15","unstructured":"Koerich, K.M., Esmailpour, M., Abdoli, S., de S. Britto, Jr., A., Koerich, A.L.: Cross-representation transferability of adversarial attacks: From spectrograms to audio waveforms. In: IJCNN (2020)"},{"key":"2_CR16","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS (2012)"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Li, Z., Wu, Y., Liu, J., Chen, Y., Yuan, B.: AdvPulse: universal, synchronization-free, and targeted audio adversarial attacks via subsecond perturbations. In: CCS (2020)","DOI":"10.1145\/3372297.3423348"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Liu, S., Liu, S., Cai, W., Pujol, S., Kikinis, R., Feng, D.: Early diagnosis of Alzheimer\u2019s disease with deep learning. In: ISBI (2014)","DOI":"10.1109\/ISBI.2014.6868045"},{"key":"2_CR19","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. In: ICLR (2017)"},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.17"},{"key":"2_CR21","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S., Fawzi, A., Frossard, P.: DeepFool: a simple and accurate method to fool deep neural networks. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.282"},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Neekhara, P., Hussain, S., Pandey, P., Dubnov, S., McAuley, J., Koushanfar, F.: Universal adversarial perturbations for speech recognition systems. In: INTERSPEECH (2019)","DOI":"10.21437\/Interspeech.2019-1353"},{"key":"2_CR23","unstructured":"van den Oord, A., et al.: WaveNet: a generative model for raw audio. In: ISCA (2016)"},{"key":"2_CR24","unstructured":"van den Oord, A., et al.: Parallel wavenet: fast high-fidelity speech synthesis. In: ICML (2018)"},{"key":"2_CR25","doi-asserted-by":"crossref","unstructured":"Papernot, N., McDaniel, P.D., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The limitations of deep learning in adversarial settings. In: IEEE EuroS &P (2016)","DOI":"10.1109\/EuroSP.2016.36"},{"key":"2_CR26","doi-asserted-by":"crossref","unstructured":"Poursaeed, O., Katsman, I., Gao, B., Belongie, S.J.: Generative adversarial perturbations. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00465"},{"key":"2_CR27","unstructured":"Prinz, K., Flexer, A.: End-to-end adversarial white box attacks on music instrument classification. arXiv abs\/2007.14714 (2020)"},{"key":"2_CR28","doi-asserted-by":"crossref","unstructured":"Ren, P., Dong, Y., Lin, S., Tong, X., Guo, B.: Image based relighting using neural networks. ACM Trans. Graph. 34(4), 111:1\u2013111:12 (2015)","DOI":"10.1145\/2766899"},{"key":"2_CR29","doi-asserted-by":"publisher","unstructured":"Ren, P., Wang, J., Gong, M., Lin, S., Tong, X., Guo, B.: Global illumination with radiance regression functions. ACM Trans. Graph. 32(4), 130:1\u2013130:12 (2013). https:\/\/doi.org\/10.1145\/2461912.2462009","DOI":"10.1145\/2461912.2462009"},{"key":"2_CR30","doi-asserted-by":"crossref","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)","DOI":"10.1109\/ICCV.2015.314"},{"key":"2_CR31","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. In: ICLR (2014)"},{"key":"2_CR32","unstructured":"Vadillo, J., Santana, R.: Universal adversarial examples in speech command classification. arXiv abs\/1911.10182 (2019)"},{"key":"2_CR33","unstructured":"Yan, W., Yu, L.: On accurate and reliable anomaly detection for gas turbine combustors: a deep learning approach. arXiv (2019)"}],"container-title":["Communications in Computer and Information Science","Emerging Information Security and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-9614-8_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T09:24:46Z","timestamp":1730971486000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-9614-8_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819996131","9789819996148"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-9614-8_2","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"4 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EISA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Emerging Information Security and Applications","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 December 2023","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":"eisa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eisa.compute.dtu.dk\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35","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":"11","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":"31% - 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)"}}]}}