{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:01:37Z","timestamp":1777388497039,"version":"3.51.4"},"reference-count":14,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T00:00:00Z","timestamp":1744675200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T00:00:00Z","timestamp":1744675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Deep neural networks (DNNs) have progressed rapidly in recent years and are increasingly deployed in real-world applications. They are now integral to critical tasks, such as traffic sign recognition in autonomous vehicles, where DNNs have become the primary method for handling most of the processing. However, many DNNs are known to be vulnerable to adversarial attacks-small but deliberately crafted perturbations applied to input data. Such perturbations can easily cause misclassification, posing significant risks, especially in autonomous vehicle systems. In this paper, we present a novel approach called multi-resolution training, which utilizes lower-resolution information from input images to retain essential features while partially filtering out adversarial attacks. Our method involves designing convolutional neural network (CNN) layers that apply various downsampling techniques with custom-designed filters, followed by upsampling to restore the resolution for further network processing. This approach has been tested on multiple DNNs, and results show that it effectively enhances the robustness of DNNs against adversarial attacks.<\/jats:p>","DOI":"10.1007\/s11760-025-04038-2","type":"journal-article","created":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T13:47:01Z","timestamp":1744724821000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multi-resolution training improves robustness against adversarial attacks"],"prefix":"10.1007","volume":"19","author":[{"given":"Shuaiang","family":"Rong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emadeldeen","family":"Hamdan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmet Enis","family":"Cetin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,15]]},"reference":[{"key":"4038_CR1","unstructured":"Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. International Conference on Learning Representations (ICLR) (2014)"},{"key":"4038_CR2","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015)"},{"key":"4038_CR3","doi-asserted-by":"crossref","unstructured":"Pavlitska, S., Lambing, N., Z\u00f6llner, J.M.: Adversarial attacks on traffic sign recognition: A survey. 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) (2023)","DOI":"10.1109\/ICECCME57830.2023.10252727"},{"key":"4038_CR4","doi-asserted-by":"crossref","unstructured":"Eykholt, K., et al.: Robust physical-world attacks on deep learning visual classification. 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00175"},{"key":"4038_CR5","doi-asserted-by":"crossref","unstructured":"Zolfi, A., Kravchik, M., Elovici, Y., Shabtai, A.: The translucent patch: A physical and universal attack on object detectors. Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.01498"},{"key":"4038_CR6","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. International Conference on Learning Representations (ICLR) (2018)"},{"key":"4038_CR7","doi-asserted-by":"crossref","unstructured":"Papernot, N., McDaniel, P.D., Goodfellow, I.J., Jha, S., Celik, Z.B., Swami, A.: Practical black-box attacks against machine learning. Asia Conference on Computer and Communications Security (AsiaCCS) (2017)","DOI":"10.1145\/3052973.3053009"},{"key":"4038_CR8","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. IEEE European Symposium on Security and Privacy (EuroS &P) (2016)","DOI":"10.1109\/EuroSP.2016.36"},{"key":"4038_CR9","doi-asserted-by":"crossref","unstructured":"Carlini, N., Wagner, D.A.: Towards evaluating the robustness of neural networks. IEEE Symposium on Security and Privacy (SP) (2017)","DOI":"10.1109\/SP.2017.49"},{"key":"4038_CR10","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Cui, J., Liu, M.: Research on adversarial patch attack defense method for traffic sign detection. In: Cyber Security. CNCERT 2022. Communications in Computer and Information Science, pp. 199\u2013210. Springer, Singapore (2022). doi: https:\/\/doi.org\/10.1007\/978-981-19-8285-9_15","DOI":"10.1007\/978-981-19-8285-9_15"},{"key":"4038_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition (2015). arXiv preprint arXiv:1512.03385","DOI":"10.1109\/CVPR.2016.90"},{"key":"4038_CR12","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: MobileNetV2: Inverted Residuals and Linear Bottlenecks (2019). arXiv preprint arXiv:1801.04381","DOI":"10.1109\/CVPR.2018.00474"},{"key":"4038_CR13","unstructured":"Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition (2015). arXiv preprint arXiv:1409.1556"},{"key":"4038_CR14","doi-asserted-by":"crossref","unstructured":"Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: The german traffic sign recognition benchmark: A multi-class classification competition. In: IEEE International Joint Conference on Neural Networks, pp. 1453\u20131460 (2011)","DOI":"10.1109\/IJCNN.2011.6033395"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04038-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-04038-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04038-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T10:38:59Z","timestamp":1747651139000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-04038-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,15]]},"references-count":14,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["4038"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-04038-2","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,15]]},"assertion":[{"value":"5 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 February 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 March 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"481"}}