{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T11:32:48Z","timestamp":1763724768398,"version":"3.40.3"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031732195"},{"type":"electronic","value":"9783031732201"}],"license":[{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-73220-1_24","type":"book-chapter","created":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T20:04:15Z","timestamp":1730577855000},"page":"412-428","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Exploring Vulnerabilities in\u00a0Spiking Neural Networks: Direct Adversarial Attacks on\u00a0Raw Event Data"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0183-5559","authenticated-orcid":false,"given":"Yanmeng","family":"Yao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2793-3526","authenticated-orcid":false,"given":"Xiaohan","family":"Zhao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7165-3143","authenticated-orcid":false,"given":"Bin","family":"Gu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,3]]},"reference":[{"key":"24_CR1","doi-asserted-by":"crossref","unstructured":"Amir, A., et\u00a0al.: A low power, fully event-based gesture recognition system. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7243\u20137252 (2017)","DOI":"10.1109\/CVPR.2017.781"},{"key":"24_CR2","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":"1","key":"24_CR3","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1137\/060676489","volume":"30","author":"BW Bader","year":"2008","unstructured":"Bader, B.W., Kolda, T.G.: Efficient matlab computations with sparse and factored tensors. SIAM J. Sci. Comput. 30(1), 205\u2013231 (2008)","journal-title":"SIAM J. Sci. Comput."},{"key":"24_CR4","unstructured":"Brown, T.B., Man\u00e9, D., Roy, A., Abadi, M., Gilmer, J.: Adversarial patch. arXiv preprint arXiv:1712.09665 (2017)"},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"Bu, T., Ding, J., Hao, Z., Yu, Z.: Rate gradient approximation attack threats deep spiking neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7896\u20137906 (2023)","DOI":"10.1109\/CVPR52729.2023.00763"},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"B\u00fcchel, J., Lenz, G., Hu, Y., Sheik, S., Sorbaro, M.: Adversarial attacks on spiking convolutional neural networks for event-based vision. arXiv preprint arXiv:2110.02929 (2021)","DOI":"10.3389\/fnins.2022.1068193"},{"key":"24_CR7","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"},{"issue":"6","key":"24_CR8","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1109\/MSP.2012.2211477","volume":"29","author":"L Deng","year":"2012","unstructured":"Deng, L.: The MNIST database of handwritten digit images for machine learning research. IEEE Signal Process. Mag. 29(6), 141\u2013142 (2012)","journal-title":"IEEE Signal Process. Mag."},{"key":"24_CR9","unstructured":"Dong, X., et al.: GreedyFool: distortion-aware sparse adversarial attack. In: Advances in Neural Information Processing Systems, vol. 33, pp. 11226\u201311236 (2020)"},{"key":"24_CR10","doi-asserted-by":"crossref","unstructured":"Fang, W., et al.: SpikingJelly: an open-source machine learning infrastructure platform for spike-based intelligence. Sci. Adv. 9(40), eadi1480 (2023)","DOI":"10.1126\/sciadv.adi1480"},{"key":"24_CR11","unstructured":"Fang, W., Yu, Z., Chen, Y., Huang, T., Masquelier, T., Tian, Y.: Deep residual learning in spiking neural networks. In: Advances in Neural Information Processing Systems, vol. 34, pp. 21056\u201321069 (2021)"},{"issue":"1","key":"24_CR12","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1109\/TPAMI.2020.3008413","volume":"44","author":"G Gallego","year":"2020","unstructured":"Gallego, G., et al.: Event-based vision: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(1), 154\u2013180 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"24_CR13","doi-asserted-by":"crossref","unstructured":"Gehrig, D., Loquercio, A., Derpanis, K.G., Scaramuzza, D.: End-to-end learning of representations for asynchronous event-based data. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5633\u20135643 (2019)","DOI":"10.1109\/ICCV.2019.00573"},{"key":"24_CR14","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781107447615","volume-title":"Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition","author":"W Gerstner","year":"2014","unstructured":"Gerstner, W., Kistler, W.M., Naud, R., Paninski, L.: Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition. Cambridge University Press, Cambridge (2014)"},{"key":"24_CR15","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)"},{"key":"24_CR16","unstructured":"Hagenaars, J., Paredes-Vall\u00e9s, F., De Croon, G.: Self-supervised learning of event-based optical flow with spiking neural networks. In: Advances in Neural Information Processing Systems, vol. 34, pp. 7167\u20137179 (2021)"},{"issue":"7","key":"24_CR17","doi-asserted-by":"publisher","first-page":"2000210","DOI":"10.1002\/aisy.202000210","volume":"3","author":"Y He","year":"2021","unstructured":"He, Y., et al.: Recent progress on emerging transistor-based neuromorphic devices. Adv. Intell. Syst. 3(7), 2000210 (2021)","journal-title":"Adv. Intell. Syst."},{"issue":"5","key":"24_CR18","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1109\/TNN.2004.832719","volume":"15","author":"EM Izhikevich","year":"2004","unstructured":"Izhikevich, E.M.: Which model to use for cortical spiking neurons? IEEE Trans. Neural Netw. 15(5), 1063\u20131070 (2004)","journal-title":"IEEE Trans. Neural Netw."},{"key":"24_CR19","unstructured":"Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144 (2016)"},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Krithivasan, S., Sen, S., Rathi, N., Roy, K., Raghunathan, A.: Efficiency attacks on spiking neural networks. In: Proceedings of the 59th ACM\/IEEE Design Automation Conference, pp. 373\u2013378 (2022)","DOI":"10.1145\/3489517.3530443"},{"key":"24_CR21","doi-asserted-by":"crossref","unstructured":"Kurakin, A., Goodfellow, I.J., Bengio, S.: Adversarial examples in the physical world. In: Artificial Intelligence Safety and Security, pp. 99\u2013112. Chapman and Hall\/CRC (2018)","DOI":"10.1201\/9781351251389-8"},{"key":"24_CR22","doi-asserted-by":"publisher","first-page":"508","DOI":"10.3389\/fnins.2016.00508","volume":"10","author":"JH Lee","year":"2016","unstructured":"Lee, J.H., Delbruck, T., Pfeiffer, M.: Training deep spiking neural networks using backpropagation. Front. Neurosci. 10, 508 (2016)","journal-title":"Front. Neurosci."},{"key":"24_CR23","doi-asserted-by":"publisher","first-page":"309","DOI":"10.3389\/fnins.2017.00309","volume":"11","author":"H Li","year":"2017","unstructured":"Li, H., Liu, H., Ji, X., Li, G., Shi, L.: Cifar10-DVS: an event-stream dataset for object classification. Front. Neurosci. 11, 309 (2017)","journal-title":"Front. Neurosci."},{"key":"24_CR24","unstructured":"Liang, L., et al.: Exploring adversarial attack in spiking neural networks with spike-compatible gradient. IEEE Trans. Neural Netw. Learn. Syst. (2021)"},{"issue":"2","key":"24_CR25","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1109\/JSSC.2007.914337","volume":"43","author":"P Lichtsteiner","year":"2008","unstructured":"Lichtsteiner, P., Posch, C., Delbruck, T.: A $$128\\times 128$$ 120 db 15 $$\\upmu $$s latency asynchronous temporal contrast vision sensor. IEEE J. Solid-State Circuits 43(2), 566\u2013576 (2008)","journal-title":"IEEE J. Solid-State Circuits"},{"key":"24_CR26","doi-asserted-by":"crossref","unstructured":"Lin, X., Dong, C., Liu, X.: SFTA: spiking neural networks vulnerable to spiking feature transferable attack. In: 2022 IEEE 21st International Conference on Ubiquitous Computing and Communications (IUCC\/CIT\/DSCI\/SmartCNS), pp. 140\u2013149. IEEE (2022)","DOI":"10.1109\/IUCC-CIT-DSCI-SmartCNS57392.2022.00033"},{"key":"24_CR27","doi-asserted-by":"crossref","unstructured":"Lin, X., Dong, C., Liu, X., Zhang, Y.: SPA: an efficient adversarial attack on spiking neural networks using spike probabilistic. In: 2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid), pp. 366\u2013375. IEEE (2022)","DOI":"10.1109\/CCGrid54584.2022.00046"},{"key":"24_CR28","doi-asserted-by":"crossref","unstructured":"Maqueda, A.I., Loquercio, A., Gallego, G., Garc\u00eda, N., Scaramuzza, D.: Event-based vision meets deep learning on steering prediction for self-driving cars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5419\u20135427 (2018)","DOI":"10.1109\/CVPR.2018.00568"},{"key":"24_CR29","doi-asserted-by":"crossref","unstructured":"Marchisio, A., Pira, G., Martina, M., Masera, G., Shafique, M.: DVS-attacks: adversarial attacks on dynamic vision sensors for spiking neural networks. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20139. IEEE (2021)","DOI":"10.1109\/IJCNN52387.2021.9534364"},{"key":"24_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1007\/978-3-642-24958-7_83","volume-title":"Neural Information Processing","author":"A Mohemmed","year":"2011","unstructured":"Mohemmed, A., Schliebs, S., Kasabov, N.: SPAN: a neuron for precise-time spike pattern association. In: Lu, B.-L., Zhang, L., Kwok, J. (eds.) ICONIP 2011. LNCS, vol. 7063, pp. 718\u2013725. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-24958-7_83"},{"key":"24_CR31","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":"24_CR32","doi-asserted-by":"publisher","first-page":"437","DOI":"10.3389\/fnins.2015.00437","volume":"9","author":"G Orchard","year":"2015","unstructured":"Orchard, G., Jayawant, A., Cohen, G.K., Thakor, N.: Converting static image datasets to spiking neuromorphic datasets using saccades. Front. Neurosci. 9, 437 (2015)","journal-title":"Front. Neurosci."},{"key":"24_CR33","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32, pp. 8024\u20138035. Curran Associates, Inc. (2019). http:\/\/papers.neurips.cc\/paper\/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf"},{"key":"24_CR34","unstructured":"Ponulak, F.: Resume-new supervised learning method for spiking neural networks. Institute of Control and Information Engineering, Pozno\u0144 University of Technology. Technical report (2005)"},{"key":"24_CR35","doi-asserted-by":"crossref","unstructured":"Vervliet, N., Debals, O., De\u00a0Lathauwer, L.: Tensorlab 3.0-numerical optimization strategies for large-scale constrained and coupled matrix\/tensor factorization. In: 2016 50th Asilomar Conference on Signals, Systems and Computers, pp. 1733\u20131738. IEEE (2016)","DOI":"10.1109\/ACSSC.2016.7869679"},{"key":"24_CR36","doi-asserted-by":"crossref","unstructured":"Zhu, A.Z., Yuan, L., Chaney, K., Daniilidis, K.: EV-FlowNet: self-supervised optical flow estimation for event-based cameras. arXiv preprint arXiv:1802.06898 (2018)","DOI":"10.15607\/RSS.2018.XIV.062"},{"key":"24_CR37","doi-asserted-by":"crossref","unstructured":"Zhu, A.Z., Yuan, L., Chaney, K., Daniilidis, K.: Unsupervised event-based learning of optical flow, depth, and egomotion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 989\u2013997 (2019)","DOI":"10.1109\/CVPR.2019.00108"},{"key":"24_CR38","doi-asserted-by":"crossref","unstructured":"Zhu, J., Zhang, T., Yang, Y., Huang, R.: A comprehensive review on emerging artificial neuromorphic devices. Appl. Phys. Rev. 7(1) (2020)","DOI":"10.1063\/1.5118217"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73220-1_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T20:07:50Z","timestamp":1730578070000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73220-1_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,3]]},"ISBN":["9783031732195","9783031732201"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73220-1_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,3]]},"assertion":[{"value":"3 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"This\u00a0study explores adversarial attacks against event-based DVS data and SNNs. This method can manipulate input data so that models produce\u00a0false outputs, affecting the security of individuals and organizations using these techniques. Compared with traditional grid-based attack methods, this method acts directly on raw event data, providing\u00a0a more direct and potentially more effective attack approach.\u00a0This innovation can lead to new security vulnerabilities that need to\u00a0be considered when designing and deploying SNNs.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Influence"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}