{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T06:38:49Z","timestamp":1776926329685,"version":"3.51.2"},"reference-count":42,"publisher":"Tech Science Press","issue":"3","license":[{"start":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T00:00:00Z","timestamp":1754179200000},"content-version":"vor","delay-in-days":214,"URL":"https:\/\/doi.org\/10.32604\/TSP-CROSSMARKPOLICY"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.067024","type":"journal-article","created":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T04:41:04Z","timestamp":1751258464000},"page":"4381-4397","update-policy":"https:\/\/doi.org\/10.32604\/tsp-crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Mitigating Adversarial Attack through Randomization Techniques and Image Smoothing"],"prefix":"10.32604","volume":"84","author":[{"given":"Hyeong-Gyeong","family":"Kim","sequence":"first","affiliation":[]},{"given":"Sang-Min","family":"Choi","sequence":"additional","affiliation":[]},{"given":"Hyeon","family":"Seo","sequence":"additional","affiliation":[]},{"given":"Suwon","family":"Lee","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TMI.2016.2553401","article-title":"Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique","volume":"35","author":"Greenspan","year":"2016","journal-title":"IEEE Trans Med Imaging"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"1510","DOI":"10.1109\/TPAMI.2017.2712608","article-title":"Long-term temporal convolutions for action recognition","volume":"40","author":"Varol","year":"2017","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref3","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"7291","article-title":"Realtime multi-person 2D pose estimation using part affinity fields","author":"Cao","year":"2017"},{"key":"ref4","unstructured":"Silva SH, Najafirad P. Opportunities and challenges in deep learning adversarial robustness: a survey. arXiv:2007.00753. 2020."},{"key":"ref5","unstructured":"Huang S, Papernot N, Goodfellow I, Duan Y, Abbeel P. Adversarial attacks on neural network policies. arXiv:1702.02284. 2017."},{"key":"ref6","series-title":"Advances in neural information processing system","article-title":"Deep defense: training DNNs with improved adversarial robustness","volume":"31","author":"Yan","year":"2018"},{"key":"ref7","series-title":"Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom); Austin, TX, USA; 2020","first-page":"1","article-title":"An analysis of adversarial attacks and defenses on autonomous driving models","author":"Deng"},{"key":"ref8","series-title":"International Conference on Learning Representations (ICLR)","article-title":"Intriguing properties of neural networks","author":"Szegedy","year":"2014"},{"key":"ref9","series-title":"International Conference on Learning Representations (ICLR)","article-title":"Explaining and harnessing adversarial examples","author":"Goodfellow","year":"2015"},{"key":"ref10","series-title":"International Conference on Learning Representations (ICLR)","article-title":"Towards deep learning models resistant to adversarial attacks","author":"Madry","year":"2018"},{"key":"ref11","series-title":"IEEE Symposium on Security and Privacy (SP)","first-page":"39","article-title":"Towards evaluating the robustness of neural networks","author":"Carlini","year":"2017"},{"key":"ref12","series-title":"Proceedings of the ACM Asia Conference on Computer and Communications Security (AsiaCCS)","first-page":"506","article-title":"Practical black-box attacks against machine learning","author":"Papernot","year":"2017"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1109\/TEVC.2019.2890858","article-title":"One pixel attack for fooling deep neural networks","volume":"23","author":"Su","year":"2019","journal-title":"IEEE Trans Evol Comput"},{"key":"ref14","series-title":"European Conference on Computer Vision (ECCV)","first-page":"484","article-title":"Square attack: a query-efficient black-box adversarial attack via random search","author":"Andriushchenko","year":"2020"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"2805","DOI":"10.1109\/TNNLS.2018.2886017","article-title":"Adversarial examples: attacks and defenses for deep learning","volume":"30","author":"Yuan","year":"2019","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"61113","DOI":"10.1109\/ACCESS.2024.3395118","article-title":"How deep learning sees the world: a survey on adversarial attacks & defenses","volume":"12","author":"Costa","year":"2024","journal-title":"IEEE Access"},{"key":"ref17","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"1000","article-title":"Adversarial camouflage: hiding physical-world attacks with natural styles","author":"Duan","year":"2020"},{"key":"ref18","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","article-title":"Fooling automated surveillance cameras: adversarial patches to attack person detection","author":"Thys","year":"2019"},{"key":"ref19","unstructured":"Carlini N, Wagner D. Defensive distillation is not robust to adversarial examples. arXiv:1607.04311. 2016."},{"key":"ref20","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"102","article-title":"Countering adversarial examples: combining input transformation and noisy training","author":"Zhang","year":"2021"},{"key":"ref21","series-title":"International Conference on Learning Representations (ICLR)","article-title":"Mitigating adversarial effects through randomization","author":"Xie","year":"2018"},{"key":"ref22","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"81","article-title":"Enhancing adversarial robustness via test-time transformation ensembling","author":"P\u00e9rez","year":"2021"},{"key":"ref23","series-title":"International Conference on Machine Learning (ICML)","first-page":"16805","article-title":"Diffusion models for adversarial purification","author":"Nie","year":"2022"},{"key":"ref24","series-title":"International Conference on Machine Learning (ICML)","first-page":"7472","article-title":"Theoretically principled trade-off between robustness and accuracy","author":"Zhang","year":"2019"},{"key":"ref25","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"6629","article-title":"Bilateral adversarial training: towards fast training of more robust models against adversarial attacks","author":"Wang","year":"2019"},{"key":"ref26","series-title":"International Conference on Learning Representations (ICLR)","article-title":"Geometry-aware instance-reweighted adversarial training","author":"Zhang","year":"2018"},{"key":"ref27","first-page":"1215","article-title":"An adversarial attack type classification method using linear discriminant analysis and k-means algorithm","volume":"31","author":"Choi","year":"2021","journal-title":"J Korea Inst Inf Secur Cryptol"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"118957","DOI":"10.1016\/j.eswa.2022.118957","article-title":"Collaborative Defense-GAN for protecting adversarial attacks on classification system","volume":"214","author":"Laykaviriyakul","year":"2023","journal-title":"Expert Syst Appl"},{"key":"ref29","series-title":"Workshop on Trustworthy and Socially Responsible Machine Learning, NeurIPS","article-title":"Densepure: understanding diffusion models towards adversarial robustness","author":"Chen","year":"2022"},{"key":"ref30","series-title":"Proceedings of the IEEE European Symposium on Security and Privacy (EuroS&P); Saarbruecken, Germany","first-page":"372","article-title":"The limitations of deep learning in adversarial settings","author":"Papernot","year":"2016"},{"key":"ref31","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"2574","article-title":"DeepFool: a simple and accurate method to fool deep neural networks","author":"Moosavi-Dezfooli","year":"2016"},{"key":"ref32","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"427","article-title":"Deep neural networks are easily fooled: high confidence predictions for unrecognizable images","author":"Nguyen","year":"2015"},{"key":"ref33","series-title":"International Conference on Learning Representations (ICLR)","article-title":"Spatially transformed adversarial examples","author":"Xiao","year":"2018"},{"key":"ref34","doi-asserted-by":"crossref","first-page":"102111","DOI":"10.1016\/j.cose.2020.102111","article-title":"Mask-guided noise restriction adversarial attacks for image classification","volume":"100","author":"Duan","year":"2021","journal-title":"Comput Secur"},{"key":"ref35","first-page":"2725","article-title":"Practical adversarial attacks imperceptible to humans in visual recognition","volume":"142","author":"Park","year":"2025","journal-title":"Comput Model Eng Sci"},{"key":"ref36","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"860","article-title":"Feature distillation: DNN-oriented JPEG compression against adversarial examples","author":"Liu","year":"2019"},{"key":"ref37","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"6528","article-title":"Barrage of random transforms for adversarially robust defense","author":"Raff","year":"2019"},{"key":"ref38","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1088\/1742-6596\/1883\/1\/012024","article-title":"Overview of image smoothing algorithms","volume":"1883","author":"Li","year":"2021","journal-title":"J Phys Conf Ser"},{"key":"ref39","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"ImageNet: A large-scale hierarchical image database","author":"Deng","year":"2009"},{"key":"ref40","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"ref41","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"2818","article-title":"Rethinking the inception architecture for computer vision","author":"Szegedy","year":"2016"},{"key":"ref42","series-title":"International Conference on Learning Representations (ICLR)","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2015"}],"container-title":["Computers, Materials & Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-84-3\/TSP_CMC_67024\/TSP_CMC_67024.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T05:45:21Z","timestamp":1776923121000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v84n3\/63202"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":42,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.067024","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"2025-04-23","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-06-12","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-30","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}