{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T01:40:18Z","timestamp":1743039618757,"version":"3.40.3"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:00:00Z","timestamp":1743033600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:00:00Z","timestamp":1743033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100014718","name":"Innovative Research Group Project of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["92167203"],"award-info":[{"award-number":["92167203"]}],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LZ22F020007"],"award-info":[{"award-number":["LZ22F020007"]}]},{"DOI":"10.13039\/501100015341","name":"Key Laboratory of Engineering Dielectrics and Its Application (Harbin University of Science and Technology), Ministry of Education","doi-asserted-by":"crossref","award":["HIK2022008"],"award-info":[{"award-number":["HIK2022008"]}],"id":[{"id":"10.13039\/501100015341","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cybersecurity"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Deep Neural Networks (DNNs) have demonstrated outstanding performance in various medical image processing tasks. However, recent studies have revealed a heightened vulnerability of medical DNNs to adversarial attacks compared to their natural counterparts. In this work, we present a novel perspective by analyzing the disparities between medical datasets and natural datasets, specifically focusing on the dataset collection process. Our analysis uncovers unique differences in the data distribution across different image classes in medical datasets, a phenomenon absent in natural datasets. To gain deeper insights into medical datasets, we employ Fourier analysis tools to investigate medical DNNs. Intriguingly, we discover that high-frequency components in medical images exhibit stronger associations with corresponding labels compared to those in natural datasets. These high-frequency components distract the attention of medical DNNs, rendering them more susceptible to adversarial images. To mitigate this vulnerability, we propose a preprocessing technique called Removing High-frequency Components (RH) training. Our experimental results demonstrate that the application of RH training significantly enhances the robustness of medical DNNs against adversarial attacks. Notably, in certain scenarios, RH training even outperforms traditional adversarial training methods, particularly when subjected to black-box attacks.<\/jats:p>","DOI":"10.1186\/s42400-024-00330-9","type":"journal-article","created":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T01:03:42Z","timestamp":1743037422000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unveiling the veil: high-frequency components as the key to understanding medical DNNs\u2019 vulnerability to adversarial examples"],"prefix":"10.1186","volume":"8","author":[{"given":"Yaguan","family":"Qian","sequence":"first","affiliation":[]},{"given":"Renhui","family":"Tao","sequence":"additional","affiliation":[]},{"given":"Huabin","family":"Du","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,27]]},"reference":[{"key":"330_CR1","doi-asserted-by":"publisher","first-page":"106755","DOI":"10.1016\/j.cmpb.2022.106755","volume":"219","author":"IA Bratchenko","year":"2022","unstructured":"Bratchenko IA, Bratchenko LA, Khristoforova YA, Moryatov AA, Kozlov SV, Zakharov VP (2022) Classification of skin cancer using convolutional neural networks analysis of raman spectra. Comput Methods Programs Biomed 219:106755","journal-title":"Comput Methods Programs Biomed"},{"issue":"12","key":"330_CR2","doi-asserted-by":"publisher","first-page":"3635","DOI":"10.1007\/s11517-022-02688-9","volume":"60","author":"H Naz","year":"2022","unstructured":"Naz H, Nijhawan R, Ahuja NJ (2022) An automated unsupervised deep learning-based approach for diabetic retinopathy detection. Medi Biol Eng Comput 60(12):3635\u20133654","journal-title":"Medi Biol Eng Comput"},{"issue":"1","key":"330_CR3","doi-asserted-by":"publisher","first-page":"102411","DOI":"10.1016\/j.ipm.2020.102411","volume":"58","author":"X Yu","year":"2021","unstructured":"Yu X, Wang S, Zhang Y (2021) Cgnet: A graph-knowledge embedded convolutional neural network for detection of pneumonia. In. Proc Manag 58(1):102411","journal-title":"In. Proc Manag"},{"key":"330_CR4","unstructured":"Gale W, Oakden-Rayner L, Carneiro G, Bradley AP, Palmer LJ (2017) Detecting hip fractures with radiologist-level performance using deep neural networks. arXiv preprint arXiv:1711.06504"},{"key":"330_CR5","unstructured":"Navarro F, Sekuboyina A, Waldmannstetter D, Peeken JC, Combs SE, Menze BH (2020) Deep reinforcement learning for organ localization in CT. In: International Conference on Medical Imaging with Deep Learning, vol. 121, pp. 544\u2013554"},{"issue":"22","key":"330_CR6","doi-asserted-by":"publisher","first-page":"2199","DOI":"10.1001\/jama.2017.14585","volume":"318","author":"BE Bejnordi","year":"2017","unstructured":"Bejnordi BE, Veta M, Van Diest PJ, Van Ginneken B, Karssemeijer N, Litjens G, Van Der Laak JA, Hermsen M, Manson QF, Balkenhol M et al (2017) Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. Jama 318(22):2199\u20132210","journal-title":"Jama"},{"key":"330_CR7","doi-asserted-by":"publisher","first-page":"98083","DOI":"10.1109\/ACCESS.2019.2930417","volume":"7","author":"X Xu","year":"2019","unstructured":"Xu X, Zhou F, Liu B, Bai X (2019) Multiple organ localization in CT image using triple-branch fully convolutional networks. IEEE Access 7:98083\u201398093","journal-title":"IEEE Access"},{"key":"330_CR8","doi-asserted-by":"crossref","unstructured":"Roth HR, Lu L, Farag A, Shin H-C, Liu J, Turkbey EB, Summers RM (2015) Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 556\u2013564","DOI":"10.1007\/978-3-319-24553-9_68"},{"key":"330_CR9","unstructured":"Finlayson SG, Chung HW, Kohane IS, Beam AL (2018) Adversarial attacks against medical deep learning systems. arXiv preprint arXiv:1804.05296"},{"key":"330_CR10","doi-asserted-by":"publisher","first-page":"107332","DOI":"10.1016\/j.patcog.2020.107332","volume":"110","author":"X Ma","year":"2021","unstructured":"Ma X, Niu Y, Gu L, Wang Y, Zhao Y, Bailey J, Lu F (2021) Understanding adversarial attacks on deep learning based medical image analysis systems. Pattern Recognit. 110:107332","journal-title":"Pattern Recognit."},{"key":"330_CR11","doi-asserted-by":"crossref","unstructured":"Koga K, Takemoto K (2022) Simple black-box universal adversarial attacks on deep neural networks for medical image classification. Algorithms, 144","DOI":"10.3390\/a15050144"},{"issue":"6433","key":"330_CR12","doi-asserted-by":"publisher","first-page":"1287","DOI":"10.1126\/science.aaw4399","volume":"363","author":"SG Finlayson","year":"2019","unstructured":"Finlayson SG, Bowers JD, Ito J, Zittrain JL, Beam AL, Kohane IS (2019) Adversarial attacks on medical machine learning. Science 363(6433):1287\u20131289","journal-title":"Science"},{"issue":"22","key":"330_CR13","doi-asserted-by":"publisher","first-page":"33773","DOI":"10.1007\/s11042-023-14702-9","volume":"82","author":"M Puttagunta","year":"2023","unstructured":"Puttagunta M, Subban R, Babu CNK (2023) Adversarial examples: attacks and defences on medical deep learning systems. Multim Tools Appl 82(22):33773\u201333809","journal-title":"Multim Tools Appl"},{"key":"330_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102141","volume":"73","author":"G Bortsova","year":"2021","unstructured":"Bortsova G, Gonz\u00e1lez-Gonzalo C, Wetstein SC, Dubost F, Katramados I, Hogeweg L, Liefers B, Ginneken B, Pluim JP, Veta M et al (2021) Adversarial attack vulnerability of medical image analysis systems: Unexplored factors. Med Image Anal 73:102141","journal-title":"Med Image Anal"},{"key":"330_CR15","doi-asserted-by":"crossref","unstructured":"Abdou MA (2022) Literature review: efficient deep neural networks techniques for medical image analysis. Neural Comput. Appl., 5791\u20135812","DOI":"10.1007\/s00521-022-06960-9"},{"key":"330_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107332","volume":"110","author":"X Ma","year":"2021","unstructured":"Ma X, Niu Y, Gu L, Wang Y, Zhao Y, Bailey J, Lu F (2021) Understanding adversarial attacks on deep learning based medical image analysis systems. Pattern Recognit 110:107332","journal-title":"Pattern Recognit"},{"key":"330_CR17","doi-asserted-by":"crossref","unstructured":"Yao Q, He Z, Lin Y, Ma K, Zheng Y, Zhou SK (2021) A hierarchical feature constraint to camouflage medical adversarial attacks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 36\u201347","DOI":"10.1007\/978-3-030-87199-4_4"},{"key":"330_CR18","unstructured":"Qi G, Gong L, Song Y, Ma K, Zheng Y (2021) Stabilized medical image attacks. In: 9th International Conference on Learning Representations"},{"key":"330_CR19","unstructured":"Xu Z-QJ, Zhang Y, Luo T, Xiao Y, Ma Z (2019) Frequency principle: Fourier analysis sheds light on deep neural networks. arXiv preprint arXiv:1901.06523"},{"key":"330_CR20","doi-asserted-by":"crossref","unstructured":"Wang H, Wu X, Huang Z, Xing EP (2020) High-frequency component helps explain the generalization of convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8684\u20138694","DOI":"10.1109\/CVPR42600.2020.00871"},{"key":"330_CR21","unstructured":"Maiya SR, Ehrlich M, Agarwal V, Lim S-N, Goldstein T, Shrivastava A (2021) A frequency perspective of adversarial robustness. arXiv preprint arXiv:2111.00861"},{"key":"330_CR22","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25"},{"key":"330_CR23","unstructured":"Krizhevsky A, Hinton G, et al (2009) Learning multiple layers of features from tiny images. Handbook of Systemic Autoimmune Diseases 1(4)"},{"key":"330_CR24","unstructured":"Kaggle (2015) Kaggle diabetic retinopathy detection challenge. https:\/\/www.kaggle.com\/c\/diabetic-retinopathy-detection"},{"key":"330_CR25","doi-asserted-by":"crossref","unstructured":"Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, et al (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama, 2402\u20132410","DOI":"10.1001\/jama.2016.17216"},{"key":"330_CR26","unstructured":"Kaggle (2019) Chest X-Ray Images (Pneumonia), 2019. https:\/\/www.kaggle.com\/paultimothymooney\/chest-xray-pneumonia"},{"key":"330_CR27","doi-asserted-by":"crossref","unstructured":"Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM (2017) Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2097\u20132106","DOI":"10.1109\/CVPR.2017.369"},{"key":"330_CR28","unstructured":"ISIC (2019) The international skin imaging collaboration. https:\/\/www.isic-archive.com"},{"key":"330_CR29","unstructured":"Paschali M, Conjeti S, Navarro F, Navab N (2018) Generalizability vs. robustness: adversarial examples for medical imaging. arXiv preprint arXiv:1804.00504"},{"key":"330_CR30","unstructured":"Ilyas A, Santurkar S, Tsipras D, Engstrom L, Tran B, Madry A (2019) Adversarial examples are not bugs, they are features. Advances in neural information processing systems 32"},{"key":"330_CR31","doi-asserted-by":"crossref","unstructured":"Kurakin A, Goodfellow IJ, Bengio S (2018) Adversarial examples in the physical world. In: Artificial Intelligence Safety and Security, pp. 99\u2013112","DOI":"10.1201\/9781351251389-8"},{"key":"330_CR32","doi-asserted-by":"crossref","unstructured":"Li Y (2023) Poisoning-based backdoor attacks in computer vision. In: Thirty-Seventh AAAI Conference on Artificial Intelligence,, pp. 16121\u201316122","DOI":"10.1609\/aaai.v37i13.26921"},{"key":"330_CR33","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","volume":"128","author":"RR Selvaraju","year":"2020","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2020) Grad-cam: Visual explanations from deep networks via gradient-based localization. Int J Comput Vis 128:336\u2013359","journal-title":"Int J Comput Vis"},{"key":"330_CR34","doi-asserted-by":"crossref","unstructured":"Carlini N, Wagner D (2017) Towards evaluating the robustness of neural networks. In: 2017 Ieee Symposium on Security and Privacy (sp), pp. 39\u201357","DOI":"10.1109\/SP.2017.49"},{"key":"330_CR35","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"330_CR36","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: 3rd International Conference on Learning Representations, ICLR"},{"key":"330_CR37","doi-asserted-by":"crossref","unstructured":"Aguiar EJD, Costa MVL, Traina C, Traina AJM (2023) Assessing vulnerabilities of deep learning explainability in medical image analysis under adversarial settings. In: International Symposium on Computer-Based Medical Systems, pp. 13\u201316","DOI":"10.1109\/CBMS58004.2023.00184"}],"container-title":["Cybersecurity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42400-024-00330-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s42400-024-00330-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42400-024-00330-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T01:04:14Z","timestamp":1743037454000},"score":1,"resource":{"primary":{"URL":"https:\/\/cybersecurity.springeropen.com\/articles\/10.1186\/s42400-024-00330-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,27]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["330"],"URL":"https:\/\/doi.org\/10.1186\/s42400-024-00330-9","relation":{},"ISSN":["2523-3246"],"issn-type":[{"value":"2523-3246","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,27]]},"assertion":[{"value":"10 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All individual patient data appearing in this study are sourced from public datasets. All participants (or their legal guardians) have been informed about the purpose, methods, and risks of the study and have consented to participate.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"We confirmed that consent has been obtained from participants (or their legal guardians) for the individual patient data included in this study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}],"article-number":"21"}}