{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:17:42Z","timestamp":1757618262261,"version":"3.44.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T00:00:00Z","timestamp":1750377600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T00:00:00Z","timestamp":1750377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"DOI":"10.1007\/s11063-025-11777-3","type":"journal-article","created":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T04:44:45Z","timestamp":1750394685000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Deep Learning with Resilient Adversarial Network (RANet): An Advanced Adversarial Resilience Training Framework for Robust Image Classification"],"prefix":"10.1007","volume":"57","author":[{"given":"Saleh","family":"Alyahyan","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,20]]},"reference":[{"key":"11777_CR1","doi-asserted-by":"crossref","unstructured":"Archana R, Jeevaraj PSE (2023) Deep learning models for digital image processing: a review, Artificial Intelligence Review. [Online]. Available: https:\/\/link.springer.com\/article\/10.1007\/s10462-023-10631-z. Accessed 11 Jul 2024","DOI":"10.1007\/s10462-023-10631-z"},{"key":"11777_CR2","unstructured":"Aarthi, Rishma (2023) Deep learning techniques for waste detection and segregation, Artificial Intelligence Review. [Online]. Available: https:\/\/link.springer.com\/article\/10.1007\/s00138-024-01519-1. Accessed 11 Jul 2024"},{"key":"11777_CR3","unstructured":"Khan K, et al. (2021) Augmentation techniques for brain MR images: a comprehensive review, Artificial Intelligence Review. [Online]. Available: https:\/\/ieeexplore.ieee.org\/stamp\/stamp.jsp?arnumber=10400453. Accessed 11 Jul 2024"},{"key":"11777_CR4","unstructured":"Alshazly M, et al (2021) Data augmentation using GANs for lung CT images in COVID-19 diagnosis, Sensors, vol 23, no 14, pp 6287\u20136299. [Online]. Available: https:\/\/www.mdpi.com\/2504-2289\/8\/1\/8. Accessed 11 Jul 2024"},{"key":"11777_CR5","unstructured":"Ibrahim S, et al (2023) COVID-19 detection using deep learning and chest X-ray images, Nature Communications, vol 23, no 14, pp 40499\u201340509. [Online]. Available: https:\/\/www.nature.com\/articles\/s41467-023-40499-0. Accessed 11 Jul 2024"},{"key":"11777_CR6","unstructured":"Sharif J, et al (2019) A hybrid deep learning approach for gastrointestinal tract infection detection using wireless capsule endoscopy images, IEEE Transactions on Medical Imaging. [Online]. Available: https:\/\/ieeexplore.ieee.org\/document\/9886997. Accessed 11 Jul 2024"},{"key":"11777_CR7","unstructured":"Alshazly Z, et al (2023) A comprehensive review on adversarial training techniques for deep learning models, Artificial Intelligence Review. [Online]. Available: https:\/\/link.springer.com\/article\/10.1007\/s11042-023-15883-z. Accessed 11 Jul 2024"},{"key":"11777_CR8","unstructured":"Zeiser T, et al (2022) Augmentation methods for breast mammography images: an overview, Neural Networks, vol 23, no 10, pp 207\u2013226. [Online]. Available: https:\/\/link.springer.com\/article\/10.1007\/s44196-023-00226-5. Accessed 11 Jul 2024"},{"key":"11777_CR9","doi-asserted-by":"crossref","unstructured":"Allen-Zhu Z, Li Y (2022) Feature purification: how adversarial training performs robust deep learning. In: Annual IEEE Symposium on Foundations of Computer Science (FOCS), pp 977\u2013988","DOI":"10.1109\/FOCS52979.2021.00098"},{"issue":"1","key":"11777_CR10","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/s11235-020-00733-2","volume":"76","author":"A Basit","year":"2021","unstructured":"Basit A, Zafar M, Liu X, Javed AR, Jalil Z, Kifayat K (2021) A comprehensive survey of ai-enabled phishing attacks detection techniques. Telecommun Syst 76(1):139\u2013154","journal-title":"Telecommun Syst"},{"key":"11777_CR11","doi-asserted-by":"crossref","unstructured":"Chen EC, Lee CR (2021) Towards fast and robust adversarial training for image classification. In: International Conference on Artificial Intelligence and Statistics, vol 12624, pp 576\u2013591","DOI":"10.1007\/978-3-030-69535-4_35"},{"key":"11777_CR12","unstructured":"Chu H (2022) Improving the robustness of deep neural networks against adversarial examples via adversarial training with maximal"},{"key":"11777_CR13","doi-asserted-by":"crossref","unstructured":"Chu H, Zhao H, Flierl M Improving the robustness of deep neural networks via adversarial training with maximal coding rate reduction. In: 2024 58th Asilomar Conference on Signals, Systems, and Computers, pp 1866-1870","DOI":"10.1109\/IEEECONF60004.2024.10942802"},{"key":"11777_CR14","first-page":"34912","volume":"35","author":"C Cianfarani","year":"2022","unstructured":"Cianfarani C, Bhagoji AN, Sehwag V, Zhao B, Zheng H, Mittal P (2022) Understanding robust learning through the lens of representation similarities. Adv Neural Inf Process Syst 35:34912\u201334925","journal-title":"Adv Neural Inf Process Syst"},{"issue":"8","key":"11777_CR15","doi-asserted-by":"publisher","first-page":"083019","DOI":"10.1088\/1367-2630\/ace8b4","volume":"25","author":"C Huang","year":"2023","unstructured":"Huang C, Zhang S (2023) Enhancing adversarial robustness of quantum neural networks by adding noise layers. New J Phys 25(8):083019","journal-title":"New J Phys"},{"key":"11777_CR16","unstructured":"Kwon M, Kim K (2023) Enhancing accuracy and robustness through adversarial training in class incremental continual learning"},{"key":"11777_CR17","doi-asserted-by":"crossref","unstructured":"Li L, Xie T, Li B (2023) Sok: certified robustness for deep neural networks. In: Proceedings of the IEEE Symposium on Security and Privacy, pp 1289\u20131310","DOI":"10.1109\/SP46215.2023.10179303"},{"key":"11777_CR18","doi-asserted-by":"crossref","unstructured":"Li P, Yi J, Zhou B, Zhang L (2019) Improving the robustness of deep neural networks via adversarial training with triplet loss. In: International Joint Conference on Artificial Intelligence (IJCAI), pp 2909\u20132915","DOI":"10.24963\/ijcai.2019\/403"},{"issue":"3","key":"11777_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pcbi.1010932","volume":"19","author":"Z Li","year":"2023","unstructured":"Li Z et al (2023) Robust deep learning object recognition models rely on low frequency information in natural images. PLoS Comput Biol 19(3):1\u201315","journal-title":"PLoS Comput Biol"},{"key":"11777_CR20","doi-asserted-by":"crossref","unstructured":"Li Z, Wang B, Xin J (2022) An integrated approach to produce robust deep neural network models with high efficiency. In: International Conference on Intelligent Computing. Springer","DOI":"10.1007\/978-3-030-95470-3_34"},{"issue":"12","key":"11777_CR21","doi-asserted-by":"publisher","first-page":"3007","DOI":"10.3390\/rs15123007","volume":"15","author":"Z Lu","year":"2023","unstructured":"Lu Z, Sun H, Xu Y (2023) Adversarial robustness enhancement of uav-oriented automatic image recognition based on deep ensemble models. Remote Sens 15(12):3007","journal-title":"Remote Sens"},{"key":"11777_CR22","unstructured":"Picot M (2023) Protecting Deep Learning Systems Against Attack: Enhancing Adversarial Robustness and Detection. PhD thesis"},{"key":"11777_CR23","unstructured":"Policy SA, Response CL, Planning R, Development R (2014) Local fiscal stress. pp 1\u201312"},{"key":"11777_CR24","unstructured":"Prins TJ (2019) UCLA UCLA Electronic Theses and Dissertations Title. PhD thesis, 2019. [Online]. Available: https:\/\/escholarship.org\/uc\/item\/0th2s0ss."},{"key":"11777_CR25","doi-asserted-by":"crossref","unstructured":"Qin Y, Hunt R, Yue C (2019) On improving the effectiveness of adversarial training. In: International Workshop on Security and Privacy Analytics (IWSPA), pp 5\u201313","DOI":"10.1145\/3309182.3309190"},{"issue":"1","key":"11777_CR26","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/s44196-023-00266-x","volume":"16","author":"B Rasheed","year":"2023","unstructured":"Rasheed B, Khattak AM, Khan A, Protasov S, Ahmad M (2023) Boosting adversarial training using robust selective data augmentation. Int J Comput Intell Syst 16(1):89","journal-title":"Int J Comput Intell Syst"},{"key":"11777_CR27","unstructured":"Rice L (2023) Methods for robust training and evaluation of deep neural networks. PhD thesis"},{"key":"11777_CR28","unstructured":"Rice L, Wong E, Kolter JZ (2020) Overfitting in adversarially robust deep learning. In: 37th International Conference on Machine Learning (ICML), pp 8049\u20138074"},{"key":"11777_CR29","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1186\/s12911-022-01891-w","volume":"22","author":"D Rodriguez","year":"2022","unstructured":"Rodriguez D, Nayak T, Chen Y, Krishnan R, Huang Y (2022) On the role of deep learning model complexity in adversarial robustness for medical images. BMC Med Inf Decis Mak 22:160","journal-title":"BMC Med Inf Decis Mak"},{"key":"11777_CR30","doi-asserted-by":"crossref","unstructured":"Ros AS, Doshi-Velez F (2018) Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients. In: 32nd AAAI Conference on Artificial Intelligence (AAAI), pp 1660\u20131669","DOI":"10.1609\/aaai.v32i1.11504"},{"issue":"1","key":"11777_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6(1):1\u201348","journal-title":"J Big Data"},{"key":"11777_CR32","unstructured":"Sitawarin C, Chakraborty S, Wagner D (2021) Sat: improving adversarial training via curriculum-based loss smoothing. In: Proceedings of the 1st Conference on Medical Imaging with Deep Learning"},{"issue":"6","key":"11777_CR33","doi-asserted-by":"publisher","first-page":"3252","DOI":"10.3390\/s23063252","volume":"23","author":"D Wang","year":"2023","unstructured":"Wang D, Jin W, Wu Y (2023) Between-class adversarial training for improving adversarial robustness of image classification. Sensors 23(6):3252","journal-title":"Sensors"},{"key":"11777_CR34","unstructured":"Wang H, Deng Y, Yoo S, Lin Y (2023) Exploring robust features for improving adversarial robustness. arXiv preprint arXiv:2309.04650."},{"key":"11777_CR35","doi-asserted-by":"crossref","unstructured":"Xue F, Peng J, Wang R, Zhang Q, Zheng WS (2019) Improving robustness of medical image diagnosis with denoising convolutional neural networks. In: International Conference on Medical Image Computing and Computer Assisted Intervention, pp 846\u2013854","DOI":"10.1007\/978-3-030-32226-7_94"},{"key":"11777_CR36","doi-asserted-by":"crossref","unstructured":"Yu C, Xue Y, Chen J, Wang Y, Ma H (2021) Enhancing adversarial robustness for image classification by regularizing class level feature distribution. In: International Conference on Image Processing (ICIP), pp 494\u2013498","DOI":"10.1109\/ICIP42928.2021.9506383"},{"issue":"8","key":"11777_CR37","doi-asserted-by":"publisher","first-page":"283","DOI":"10.3390\/a15080283","volume":"15","author":"W Zhao","year":"2022","unstructured":"Zhao W, Alwidian S, Mahmoud QH (2022) Adversarial training methods for deep learning: a systematic review. Algorithms 15(8):283","journal-title":"Algorithms"},{"key":"11777_CR38","doi-asserted-by":"crossref","unstructured":"Zheng S, Song Y, Leung T, Goodfellow I (2016) Improving the robustness of deep neural networks via stability training. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 4480\u20134488","DOI":"10.1109\/CVPR.2016.485"},{"issue":"2","key":"11777_CR39","first-page":"2639","volume":"70","author":"V Praveena","year":"2022","unstructured":"Praveena V, Vijayaraj A, Chinnasamy P, Ali I, Alroobaea R, Alyahyan SY (2022) Optimal deep reinforcement learning for intrusion detection in UAVs. Comput Mater Contin 70(2):2639\u20132653","journal-title":"Comput Mater Contin"},{"key":"11777_CR40","first-page":"4085","volume":"75","author":"AA Khan","year":"2023","unstructured":"Khan AA, Jahangir R, Alroobaea R, Alyahyan SY, Almulhi AH (2023) An efficient text-independent speaker identification using feature fusion and transformer model. Comput Mater Contin 75:4085\u20134100","journal-title":"Comput Mater Contin"},{"key":"11777_CR41","doi-asserted-by":"publisher","unstructured":"Ding Z, Tian Y, Wang G, Xiong J (2024) Regularization mixup adversarial training: a defense strategy for membership privacy with model availability assurance. In: 2024 2nd International Conference on Big Data and Privacy Computing (BDPC), Macau, China, 2024, pp 206\u2013212. https:\/\/doi.org\/10.1109\/BDPC59998.2024.10649357","DOI":"10.1109\/BDPC59998.2024.10649357"},{"key":"11777_CR42","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1007\/s12530-025-09676-y","volume":"16","author":"L Dhamija","year":"2025","unstructured":"Dhamija L, Bansal U (2025) Correction: AFLF: a defensive framework to defeat multifaceted adversarial attacks via attention feature fusion. Evol Syst 16:50. https:\/\/doi.org\/10.1007\/s12530-025-09676-y","journal-title":"Evol Syst"},{"issue":"2","key":"11777_CR43","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.32604\/cmc.2024.057866","volume":"82","author":"Y Zhu","year":"2025","unstructured":"Zhu Y, Yang H, Zhu B (2025) Exploratory research on defense against natural adversarial examples in image classification. Comput Mater Contin 82(2):1947\u20131968. https:\/\/doi.org\/10.32604\/cmc.2024.057866","journal-title":"Comput Mater Contin"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-025-11777-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-025-11777-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-025-11777-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T20:30:44Z","timestamp":1757190644000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-025-11777-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,20]]},"references-count":43,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["11777"],"URL":"https:\/\/doi.org\/10.1007\/s11063-025-11777-3","relation":{},"ISSN":["1573-773X"],"issn-type":[{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2025,6,20]]},"assertion":[{"value":"20 May 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This research study solely involves the use of historical datasets. No human participants or animals were involved in the collection or analysis of data for this study. As a result, ethical approval was not required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human or Animal Rights"}},{"value":"No informed consent procedures were conducted since this research study did not involve human participants or animals. The data used for analysis was publicly available and did not require informed consent.The author affirms their commitment to conducting research according to the highest ethical standards and ensuring the accuracy, transparency, and reliability of the presented findings.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}],"article-number":"60"}}