{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:42:52Z","timestamp":1772044972833,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Industrial Internet Research Institute (Jinan) of Beijing University of Posts and Telecommunications","award":["201915001"],"award-info":[{"award-number":["201915001"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Sign Process Syst"],"published-print":{"date-parts":[[2021,7]]},"DOI":"10.1007\/s11265-020-01634-y","type":"journal-article","created":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T05:02:41Z","timestamp":1612155761000},"page":"709-718","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Adversarial Attacks on Voice Recognition Based on Hyper Dimensional Computing"],"prefix":"10.1007","volume":"93","author":[{"given":"Wencheng","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,1]]},"reference":[{"issue":"7553","key":"1634_CR1","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436.","journal-title":"Nature"},{"key":"1634_CR2","doi-asserted-by":"crossref","unstructured":"Qiu, H., Zheng, Q., Memmi, G., Lu, J., Qiu, M., & Thuraisingham, B. (2020). Deep residual learning based enhanced JPEG compression in the internet of things. IEEE Transactions on Industrial Informatics.","DOI":"10.1109\/TII.2020.2994743"},{"key":"1634_CR3","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1016\/j.future.2015.09.021","volume":"56","author":"A Botta","year":"2016","unstructured":"Botta, A., De Donato, W., Persico, V., & Pescap\u00e9, A. (2016). Integration of cloud computing and internet of things: a survey. Future Generation Computer Systems, 56, 684\u2013700.","journal-title":"Future Generation Computer Systems"},{"key":"1634_CR4","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.inffus.2019.02.002","volume":"50","author":"H Qiu","year":"2019","unstructured":"Qiu, H., Qiu, M., Lu, Z., & Gerard, M. (2019). An efficient key distribution system for data fusion in V2X heterogeneous networks. Information Fusion, 50, 212\u2013220.","journal-title":"Information Fusion"},{"issue":"4","key":"1634_CR5","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1016\/j.bushor.2015.03.008","volume":"58","author":"I Lee","year":"2015","unstructured":"Lee, I., & Lee, K. (2015). The internet of things (iot): applications, investments, and challenges for enterprises. Business Horizons, 58(4), 431\u2013440.","journal-title":"Business Horizons"},{"key":"1634_CR6","doi-asserted-by":"crossref","unstructured":"Fraga-Lamas, P., Fern\u00e1ndez-Caram\u00e9s, M.T., & Castedo, L. (2017). Towards the internet of smart trains: a review on industrial IoT-connected railways. Sensors.","DOI":"10.3390\/s17061457"},{"key":"1634_CR7","unstructured":"Bengio, E., Bacon, P.-L., Pineau, J., & Precup, D. (2015). Conditional computation in neural networks for faster models. arXiv:1511.06297."},{"issue":"4","key":"1634_CR8","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1016\/j.jpdc.2007.06.014","volume":"68","author":"M Qiu","year":"2008","unstructured":"Qiu, M., Sha, E.H.-M., Liu, M., Lin, M., Hua, S., & Yang, L.T. (2008). Energy minimization with loop fusion and multi-functional-unit scheduling for multidimensional DSP. Journal of Parallel and Distributed Computing, 68(4), 443\u2013455.","journal-title":"Journal of Parallel and Distributed Computing"},{"issue":"2","key":"1634_CR9","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/s12559-009-9009-8","volume":"1","author":"P Kanerva","year":"2009","unstructured":"Kanerva, P. (2009). Hyperdimensional computing: an introduction to computing in distributed representation with high-dimensional random vectors. Cognitive Computation, 1(2), 139\u2013159.","journal-title":"Cognitive Computation"},{"key":"1634_CR10","doi-asserted-by":"crossref","unstructured":"Rahimi, A., Kanerva, P., & Rabaey, J.M. (2016). A robust and energy-efficient classifier using brain-inspired hyperdimensional computing. In Proceedings of the 2016 international symposium on low power electronics and design (pp. 64\u201369).","DOI":"10.1145\/2934583.2934624"},{"key":"1634_CR11","unstructured":"Kanerva, P. (2010). What we mean when we say \u2018What\u2019s the dollar of mexico?\u2019: prototypes and mapping in concept space. In 2010 AAAI fall symposium series."},{"key":"1634_CR12","unstructured":"Najafabadi, F.R., Rahimi, A., Kanerva, P., & Rabaey, J.M. (2016). Hyperdimensional computing for text classification. In Design, automation test in Europe conference exhibition (DATE), University Booth (pp. 1\u20131)."},{"key":"1634_CR13","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.inffus.2019.07.012","volume":"55","author":"H Qiu","year":"2020","unstructured":"Qiu, H., Qiu, M., & Lu, Z. (2020). Selective encryption on ecg data in body sensor network based on supervised machine learning. Information Fusion, 55, 59\u201367.","journal-title":"Information Fusion"},{"issue":"6","key":"1634_CR14","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1109\/MDAT.2017.2740839","volume":"34","author":"M Imani","year":"2017","unstructured":"Imani, M., Hwang, J., Rosing, T., Rahimi, A., & Rabaey, J.M. (2017). Low-power sparse hyperdimensional encoder for language recognition. IEEE Design & Test, 34(6), 94\u2013101.","journal-title":"IEEE Design & Test"},{"key":"1634_CR15","unstructured":"R\u00e4s\u00e4nen, O.J. (2015). Generating hyperdimensional distributed representations from continuous-valued multivariate sensory input. In CogSci."},{"key":"1634_CR16","unstructured":"Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013). Intriguing properties of neural networks. arXiv:1312.6199."},{"key":"1634_CR17","doi-asserted-by":"crossref","unstructured":"Evtimov, I., Eykholt, K., Fernandes, E., Kohno, T., Li, B., Prakash, A., Rahmati, A., & Song, D. (2017). Robust physical-world attacks on deep learning models. arXiv:1707.08945.","DOI":"10.1109\/CVPR.2018.00175"},{"key":"1634_CR18","doi-asserted-by":"crossref","unstructured":"Xie, C., Wang, J., Zhang, Z., Zhou, Y., Xie, L., & Yuille, A. (2017). Adversarial examples for semantic segmentation and object detection. In Proceedings of the IEEE international conference on computer vision (pp. 1369\u20131378).","DOI":"10.1109\/ICCV.2017.153"},{"key":"1634_CR19","doi-asserted-by":"crossref","unstructured":"Taori, R., Kamsetty, A., Chu, B., & Vemuri, N. (2019). Targeted adversarial examples for black box audio systems. In 2019 IEEE security and privacy workshops (SPW) (pp. 15\u201320): IEEE.","DOI":"10.1109\/SPW.2019.00016"},{"key":"1634_CR20","doi-asserted-by":"crossref","unstructured":"Carlini, N., & Wagner, D. (2018). Audio adversarial examples: Targeted attacks on speech-to-text. In 2018 IEEE security and privacy workshops (SPW) (pp. 1\u20137): IEEE.","DOI":"10.1109\/SPW.2018.00009"},{"key":"1634_CR21","doi-asserted-by":"crossref","unstructured":"Yakura, H., & Sakuma, J. (2018). Robust audio adversarial example for a physical attack. arXiv:1810.11793.","DOI":"10.24963\/ijcai.2019\/741"},{"key":"1634_CR22","doi-asserted-by":"crossref","unstructured":"Li, J., Ji, S., Du, T., Li, B., & Wang, T. (2018). Textbugger: generating adversarial text against real-world applications. arXiv:1812.05271.","DOI":"10.14722\/ndss.2019.23138"},{"key":"1634_CR23","unstructured":"Liu, X., Lin, Y., Li, H., & Zhang, J. (2018). Adversarial examples: attacks on machine learning-based malware visualization detection methods. arXiv:1808.01546."},{"key":"1634_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, E.W., Sheng, Z.Q., Alhazmi, A., & Li, C. (2020). Adversarial attacks on deep-learning models in natural language processing: a survey. ACM Transactions on Intelligent Systems and Technology, 1\u201341.","DOI":"10.1145\/3374217"},{"key":"1634_CR25","doi-asserted-by":"crossref","unstructured":"Imani, M., Kong, D., Rahimi, A., & Rosing, T. (2017). VoiceHD: hyperdimensional computing for efficient speech recognition. In 2017 IEEE international conference on rebooting computing (ICRC) (pp. 1\u20138): IEEE.","DOI":"10.1109\/ICRC.2017.8123650"},{"key":"1634_CR26","doi-asserted-by":"crossref","unstructured":"Poddar, V., Chatterjee, B., Nandi, D., Ghosh, B., & Mondal, S. (2018). Data capturing and modeling by speech recognition - roles demonstrated by artificial intelligence, a survey. UEMCON, 1088\u20131092.","DOI":"10.1109\/UEMCON.2018.8796683"},{"key":"1634_CR27","doi-asserted-by":"crossref","unstructured":"Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 1735\u20131780.","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"1634_CR28","doi-asserted-by":"crossref","unstructured":"Bahdanau, D., Chorowski, J., Serdyuk, D., Brakel, P., & Bengio, Y. (2016). End-to-end attention-based large vocabulary speech recognition. In 2016 IEEE international conference acoustics, speech and signal processing (pp. 4945\u20134949).","DOI":"10.1109\/ICASSP.2016.7472618"},{"key":"1634_CR29","unstructured":"Smith, L., & Gal, Y. (2018). Understanding measures of uncertainty for adversarial example detection. UAI, 560\u2013569."},{"key":"1634_CR30","unstructured":"Goodfellow, I.J., Shlens, J., & Szegedy, C. (2014). Explaining and harnessing adversarial examples. arXiv:1412.6572."},{"key":"1634_CR31","doi-asserted-by":"crossref","unstructured":"Carlini, N., & Wagner, D. (2017). Towards evaluating the robustness of neural networks. In 2017 IEEE symposium on security and privacy (SP) (pp. 39\u201357): IEEE.","DOI":"10.1109\/SP.2017.49"},{"key":"1634_CR32","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.-M., Fawzi, A., & Frossard, P. (2016). 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).","DOI":"10.1109\/CVPR.2016.282"},{"key":"1634_CR33","doi-asserted-by":"crossref","unstructured":"Storn, R., & Price, V.K. (1997). Differential evolution \u2013 a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 341\u2013359.","DOI":"10.1023\/A:1008202821328"},{"key":"1634_CR34","doi-asserted-by":"crossref","unstructured":"Su, J., Vargas, V.D., & Sakurai, K. (2019). One pixel attack for fooling deep neural networks. IEEE Transactions on Evolutionary Computation, 828\u2013841.","DOI":"10.1109\/TEVC.2019.2890858"},{"key":"1634_CR35","unstructured":"Fanty, M., Cole, R., & Muthusamy, Y. (1994). The isolet spoken letter database."},{"key":"1634_CR36","unstructured":"Fanty, A.M., & Cole, R. (1990). Spoken letter recognition. NIPS, 220\u2013226."},{"issue":"4","key":"1634_CR37","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1109\/TC.2006.59","volume":"55","author":"Z Shao","year":"2006","unstructured":"Shao, Z., Xue, C., Zhuge, Q., Qiu, M., Xiao, B., & Sha, E.-M. (2006). Security protection and checking for embedded system integration against buffer overflow attacks via hardware\/software. IEEE Transactions on Computers, 55(4), 443\u2013453.","journal-title":"IEEE Transactions on Computers"},{"issue":"9","key":"1634_CR38","doi-asserted-by":"publisher","first-page":"840","DOI":"10.1016\/j.sysarc.2011.03.005","volume":"57","author":"J Li","year":"2011","unstructured":"Li, J., Ming, Z., Qiu, M., Quan, G., Qin, X., & Chen, T. (2011). Resource allocation robustness in multi-core embedded systems with inaccurate information. Journal of Systems Architecture, 57(9), 840\u2013849.","journal-title":"Journal of Systems Architecture"},{"key":"1634_CR39","doi-asserted-by":"crossref","unstructured":"Gai, K., Qiu, M., & Zhao, H. (2017). Privacy-preserving data encryption strategy for big data in mobile cloud computing. IEEE Transactions on Big Data, 1\u20131.","DOI":"10.1109\/TBDATA.2017.2705807"},{"key":"1634_CR40","doi-asserted-by":"crossref","unstructured":"Qiu, H., Noura, H., Qiu, M., Zhong, M., & Memmi, G. (2019). A user-centric data protection method for cloud storage based on invertible DWT. IEEE Transactions on Cloud Computing, 1\u20131.","DOI":"10.1109\/TCC.2019.2911679"}],"container-title":["Journal of Signal Processing Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11265-020-01634-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11265-020-01634-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11265-020-01634-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,14]],"date-time":"2021-06-14T17:09:36Z","timestamp":1623690576000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11265-020-01634-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,1]]},"references-count":40,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2021,7]]}},"alternative-id":["1634"],"URL":"https:\/\/doi.org\/10.1007\/s11265-020-01634-y","relation":{},"ISSN":["1939-8018","1939-8115"],"issn-type":[{"value":"1939-8018","type":"print"},{"value":"1939-8115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,1]]},"assertion":[{"value":"11 June 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 February 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}