{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T16:13:12Z","timestamp":1772122392572,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":27,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T00:00:00Z","timestamp":1702252800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"AIDE project funded by the Belgian SPF BOSA"},{"name":"Flemish Research Programme Cybersecurity"},{"name":"Research Fund KU Leuven"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,12,11]]},"DOI":"10.1145\/3630180.3631201","type":"proceedings-article","created":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T18:16:31Z","timestamp":1700158591000},"page":"7-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["On the adversarial robustness of full integer quantized TinyML models at the edge"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6279-4430","authenticated-orcid":false,"given":"Davy","family":"Preuveneers","sequence":"first","affiliation":[{"name":"DistriNet, KU Leuven, Leuven, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3836-1840","authenticated-orcid":false,"given":"Willem","family":"Verheyen","sequence":"additional","affiliation":[{"name":"DistriNet, KU Leuven, Leuven, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9292-6449","authenticated-orcid":false,"given":"Sander","family":"Joos","sequence":"additional","affiliation":[{"name":"DistriNet, KU Leuven, Leuven, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7710-5092","authenticated-orcid":false,"given":"Wouter","family":"Joosen","sequence":"additional","affiliation":[{"name":"DistriNet, KU Leuven, Leuven, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,12,11]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2014.2320099"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CW.2019.00057"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Nicholas Carlini and David Wagner. 2017. Towards Evaluating the Robustness of Neural Networks. arXiv:1608.04644 [cs.CR]","DOI":"10.1109\/SP.2017.49"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"e_1_3_2_1_5_1","volume-title":"In\u00e1cio","author":"Costa Joana C.","year":"2023","unstructured":"Joana C. Costa, Tiago Roxo, Hugo Proen\u00e7a, and Pedro R. M. In\u00e1cio. 2023. How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses. arXiv:2305.10862 [cs.CV]"},{"key":"e_1_3_2_1_6_1","first-page":"800","article-title":"Tensorflow lite micro: Embedded machine learning for tinyml systems","volume":"3","author":"David Robert","year":"2021","unstructured":"Robert David, Jared Duke, Advait Jain, Vijay Janapa Reddi, Nat Jeffries, Jian Li, Nick Kreeger, Ian Nappier, Meghna Natraj, Tiezhen Wang, et al. 2021. Tensorflow lite micro: Embedded machine learning for tinyml systems. Proceedings of Machine Learning and Systems 3 (2021), 800--811.","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2021.100461"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Amir Gholami Sehoon Kim Zhen Dong Zhewei Yao Michael W. Mahoney and Kurt Keutzer. 2021. A Survey of Quantization Methods for Efficient Neural Network Inference. arXiv:2103.13630 [cs.CV]","DOI":"10.1201\/9781003162810-13"},{"key":"e_1_3_2_1_9_1","unstructured":"Ian J. Goodfellow Jonathon Shlens and Christian Szegedy. 2015. Explaining and Harnessing Adversarial Examples. arXiv:1412.6572 [stat.ML]"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICAIIC54071.2022.9722636"},{"key":"e_1_3_2_1_11_1","volume-title":"Adversarial examples are not bugs, they are features. Advances in neural information processing systems 32","author":"Ilyas Andrew","year":"2019","unstructured":"Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry. 2019. Adversarial examples are not bugs, they are features. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-16740-9"},{"key":"e_1_3_2_1_13_1","volume-title":"Adversarial machine learning at scale. arXiv preprint arXiv:1611.01236","author":"Kurakin Alexey","year":"2016","unstructured":"Alexey Kurakin, Ian Goodfellow, and Samy Bengio. 2016. Adversarial machine learning at scale. arXiv preprint arXiv:1611.01236 (2016)."},{"key":"e_1_3_2_1_14_1","unstructured":"Yann LeCun and Corinna Cortes. 2010. MNIST handwritten digit database. http:\/\/yann.lecun.com\/exdb\/mnist\/. (2010). http:\/\/yann.lecun.com\/exdb\/mnist\/"},{"key":"e_1_3_2_1_15_1","volume-title":"Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083","author":"Madry Aleksander","year":"2017","unstructured":"Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu.2017. Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 (2017)."},{"key":"e_1_3_2_1_16_1","unstructured":"Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras and Adrian Vladu. 2019. Towards Deep Learning Models Resistant to Adversarial Attacks. arXiv:1706.06083 [stat.ML]"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi and Pascal Frossard. 2016. DeepFool: a simple and accurate method to fool deep neural networks. arXiv:1511.04599 [cs.LG]","DOI":"10.1109\/CVPR.2016.282"},{"key":"e_1_3_2_1_18_1","volume-title":"Beat Buesser, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Nathalie Baracaldo, Bryant Chen, Heiko Ludwig, Ian M. Molloy, and Ben Edwards.","author":"Nicolae Maria-Irina","year":"2019","unstructured":"Maria-Irina Nicolae, Mathieu Sinn, Minh Ngoc Tran, Beat Buesser, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Nathalie Baracaldo, Bryant Chen, Heiko Ludwig, Ian M. Molloy, and Ben Edwards. 2019. Adversarial Robustness Toolbox v1.0.0. arXiv:1807.01069 [cs.LG]"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583683"},{"key":"e_1_3_2_1_20_1","volume-title":"Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein.","author":"Shafahi Ali","year":"2019","unstructured":"Ali Shafahi, Mahyar Najibi, Mohammad Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein. 2019. Adversarial training for free! Advances in Neural Information Processing Systems 32 (2019)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3555776.3577747"},{"key":"e_1_3_2_1_22_1","volume-title":"The space of transferable adversarial examples. arXiv preprint arXiv:1704.03453","author":"Tram\u00e8r Florian","year":"2017","unstructured":"Florian Tram\u00e8r, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel. 2017. The space of transferable adversarial examples. arXiv preprint arXiv:1704.03453 (2017)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503823.3503836"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3575879.3575994"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/MASSW.2019.00009"},{"key":"e_1_3_2_1_26_1","volume-title":"TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-low-power Microcontrollers","author":"Warden Pete","unstructured":"Pete Warden and Daniel Situnayake. 2019. TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-low-power Microcontrollers. O'Reilly Media."},{"key":"e_1_3_2_1_27_1","unstructured":"Han Xiao Kashif Rasul and Roland Vollgraf. 2017. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. arXiv:1708.07747 [cs.LG]"}],"event":{"name":"Middleware '23: 24th International Middleware Conference","location":"Bologna Italy","acronym":"Middleware '23","sponsor":["ACM Association for Computing Machinery","IFIP International Federation for Information Processing"]},"container-title":["Proceedings of the 2nd International Workshop on Middleware for the Edge"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3630180.3631201","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3630180.3631201","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T20:34:59Z","timestamp":1755981299000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3630180.3631201"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,11]]},"references-count":27,"alternative-id":["10.1145\/3630180.3631201","10.1145\/3630180"],"URL":"https:\/\/doi.org\/10.1145\/3630180.3631201","relation":{},"subject":[],"published":{"date-parts":[[2023,12,11]]},"assertion":[{"value":"2023-12-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}