{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T17:40:43Z","timestamp":1762364443573,"version":"build-2065373602"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T00:00:00Z","timestamp":1762300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T00:00:00Z","timestamp":1762300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["05D23UM1","05D23UM1","05D23UM1","05D23UM1","05D23UM1","05D23UM1","05D23UM1","05D23UM1"],"award-info":[{"award-number":["05D23UM1","05D23UM1","05D23UM1","05D23UM1","05D23UM1","05D23UM1","05D23UM1","05D23UM1"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008131","name":"Rheinische Friedrich-Wilhelms-Universit\u00e4t Bonn","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100008131","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Comput Softw Big Sci"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Correlations between input parameters play a crucial role in many scientific classification tasks, since these are often related to fundamental laws of nature. For example, in high energy physics, one common deep learning use-case is the classification of signal and background processes in particle collisions. In many such cases, the fundamental principles of the correlations between observables are often better understood than the actual distributions of the observables themselves. In this work, we present a new adversarial attack algorithm called\n                    <jats:italic>Random Distribution Shuffle Attack (RDSA)<\/jats:italic>\n                    , emphasizing the correlations between observables in the network rather than individual feature characteristics. Correct application of the proposed novel attack can result in a significant improvement in classification performance\u2014particularly in the context of data augmentation\u2014when using the generated adversaries within adversarial training. Given that correlations between input features are also crucial in many other disciplines, we demonstrate the RDSA effectiveness on six classification tasks, including two particle collision challenges (using CERN Open Data), hand-written digit recognition (MNIST784), human activity recognition (HAR), weather forecasting (Rain in Australia), and ICU patient mortality (MIMIC-IV), demonstrating a general use case beyond fundamental physics for this new type of adversarial attack algorithm.\n                  <\/jats:p>","DOI":"10.1007\/s41781-025-00148-1","type":"journal-article","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T17:35:38Z","timestamp":1762364138000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations"],"prefix":"10.1007","volume":"9","author":[{"given":"Lucie","family":"Flek","sequence":"first","affiliation":[]},{"given":"Philipp Alexander","family":"Jung","sequence":"additional","affiliation":[]},{"given":"Akbar","family":"Karimi","sequence":"additional","affiliation":[]},{"given":"Timo","family":"Saala","sequence":"additional","affiliation":[]},{"given":"Alexander","family":"Schmidt","sequence":"additional","affiliation":[]},{"given":"Matthias","family":"Schott","sequence":"additional","affiliation":[]},{"given":"Philipp","family":"Soldin","sequence":"additional","affiliation":[]},{"given":"Christopher","family":"Wiebusch","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,5]]},"reference":[{"key":"148_CR1","unstructured":"Feickert M, Nachman B (2021) A Living Review of Machine Learning for Particle Physics https:\/\/arxiv.org\/abs\/2102.02770arXiv:2102.02770 [hep-ph]"},{"issue":"2","key":"148_CR2","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1085\/2\/022008","volume":"1085","author":"K Albertsson","year":"2018","unstructured":"Albertsson K (2018) Others: machine Learning in High Energy Physics Community White Paper. J Phys Conf Ser 1085(2):022008. https:\/\/doi.org\/10.1088\/1742-6596\/1085\/2\/022008. arXiv:1807.02876 [physics.comp-ph]","journal-title":"J Phys Conf Ser"},{"key":"148_CR3","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1146\/annurev-nucl-101917-021019","volume":"68","author":"D Guest","year":"2018","unstructured":"Guest D, Cranmer K, Whiteson D (2018) Deep Learning and its Application to LHC Physics. Ann Rev Nucl Part Sci 68:161\u2013181. https:\/\/doi.org\/10.1146\/annurev-nucl-101917-021019. arXiv:1806.11484 [hep-ex]","journal-title":"Ann Rev Nucl Part Sci"},{"key":"148_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.revip.2024.100091","volume":"12","author":"V Belis","year":"2024","unstructured":"Belis V, Odagiu P, Aarrestad TK (2024) Machine learning for anomaly detection in particle physics. Rev Phys 12:100091. https:\/\/doi.org\/10.1016\/j.revip.2024.100091. arXiv:2312.14190 [physics.data-an]","journal-title":"Rev Phys"},{"key":"148_CR5","unstructured":"Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, Fergus R (2014) Intriguing properties of neural networks arXiv:1312.6199 [cs.CV]"},{"key":"148_CR6","unstructured":"Goodfellow IJ, Shlens J, Szegedy C (2015) Explaining and Harnessing Adversarial Examples. arXiv:1412.6572 [stat.ML]"},{"key":"148_CR7","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli S-M, Fawzi A, Frossard P(2016) DeepFool: a simple and accurate method to fool deep neural networks arXiv:1511.04599 [cs.CV]","DOI":"10.1109\/CVPR.2016.282"},{"key":"148_CR8","unstructured":"Madry A, Makelov A, Schmidt L, Tsipras D, Vladu A (2019) Towards Deep Learning Models Resistant to Adversarial Attacks arXiv:1706.06083 [stat.ML]"},{"key":"148_CR9","doi-asserted-by":"publisher","first-page":"08004","DOI":"10.1088\/1748-0221\/3\/08\/S08004","volume":"3","author":"S Chatrchyan","year":"2008","unstructured":"Chatrchyan S et al (2008) The CMS Experiment at the CERN LHC. JINST 3:08004. https:\/\/doi.org\/10.1088\/1748-0221\/3\/08\/S08004","journal-title":"JINST"},{"key":"148_CR10","doi-asserted-by":"publisher","unstructured":"Evans L, Bryant P (eds.) (2008) LHC Machine. JINST 3, 08001 https:\/\/doi.org\/10.1088\/1748-0221\/3\/08\/S08001","DOI":"10.1088\/1748-0221\/3\/08\/S08001"},{"key":"148_CR11","unstructured":"Ballet V, Renard X, Aigrain J, Laugel T, Frossard P, Detyniecki M (2019) Imperceptible Adversarial Attacks on Tabular Data. arXiv:1911.03274 [stat.ML]"},{"key":"148_CR12","doi-asserted-by":"crossref","unstructured":"Kong Z, Guo J, Li A, Liu C (2021) PhysGAN: Generating Physical-World-Resilient Adversarial Examples for Autonomous Driving arXiv:1907.04449 [cs.CV]","DOI":"10.1109\/CVPR42600.2020.01426"},{"key":"148_CR13","unstructured":"Xu L, Skoularidou M, Cuesta-Infante A, Veeramachaneni K (2019) Modeling Tabular data using Conditional GAN arXiv:1907.00503 [cs.LG]"},{"key":"148_CR14","unstructured":"CERN: CERN Open Data Portal (2024). https:\/\/opendata.cern.ch\/ Accessed 2024-02-19"},{"key":"148_CR15","doi-asserted-by":"publisher","unstructured":"CMS collaboration: simulated dataset VBFToHToWWToLAndTauNuQQ_M-125_8TeV-powheg-pythia6 in AODSIM format for 2012 collision data. CERN Open Data Portal (2017). https:\/\/doi.org\/10.7483\/OPENDATA.CMS.YA1K.II60","DOI":"10.7483\/OPENDATA.CMS.YA1K.II60"},{"key":"148_CR16","doi-asserted-by":"publisher","unstructured":"CMS collaboration: simulated dataset LplusNuVBF_Mqq-120_8TeV-madgraph in AODSIM format for 2012 collision data. CERN Open Data Portal (2017). https:\/\/doi.org\/10.7483\/OPENDATA.CMS.YN3L.COMS","DOI":"10.7483\/OPENDATA.CMS.YN3L.COMS"},{"key":"148_CR17","doi-asserted-by":"publisher","unstructured":"CMS collaboration: simulated dataset WpWmJJToLNuQQ_TuneZ2star_8TeV-vbfnlo-pythia6 in AODSIM format for 2012 collision data. CERN Open Data Portal (2017). https:\/\/doi.org\/10.7483\/OPENDATA.CMS.52HO.31V8","DOI":"10.7483\/OPENDATA.CMS.52HO.31V8"},{"key":"148_CR18","doi-asserted-by":"publisher","unstructured":"CMS collaboration: simulated dataset WpWmJJToQQLNuBar_TuneZ2star_8TeV-vbfnlo-pythia6 in AODSIM format for 2012 collision data. CERN Open Data Portal (2017). https:\/\/doi.org\/10.7483\/OPENDATA.CMS.4HOX.06QU","DOI":"10.7483\/OPENDATA.CMS.4HOX.06QU"},{"key":"148_CR19","doi-asserted-by":"publisher","unstructured":"CMS collaboration: simulated dataset ZJetToMuMu_Pt-80to120_TuneEE3C_8TeV_herwigpp in AODSIM format for 2012 collision data. CERN Open Data Portal (2017). https:\/\/doi.org\/10.7483\/OPENDATA.CMS.TYJU.X3NA","DOI":"10.7483\/OPENDATA.CMS.TYJU.X3NA"},{"key":"148_CR20","doi-asserted-by":"publisher","unstructured":"CMS collaboration: simulated dataset TTJets_FullLeptMGDecays_TuneP11TeV_8TeV-madgraph-tauola in AODSIM format for 2012 collision data. CERN Open Data Portal (2017). https:\/\/doi.org\/10.7483\/OPENDATA.CMS.7RZ3.0BXP","DOI":"10.7483\/OPENDATA.CMS.7RZ3.0BXP"},{"key":"148_CR21","doi-asserted-by":"publisher","unstructured":"CMS collaboration: simulated dataset WWJetsTo2L2Nu_TuneZ2star_8TeV-madgraph-tauola in AODSIM format for 2012 collision data. CERN Open Data Portal (2017). https:\/\/doi.org\/10.7483\/OPENDATA.CMS.V2C6.O1P4","DOI":"10.7483\/OPENDATA.CMS.V2C6.O1P4"},{"key":"148_CR22","doi-asserted-by":"publisher","DOI":"10.1140\/epjc\/s10052-020-08629-w","author":"VS Ngairangbam","year":"2020","unstructured":"Ngairangbam VS, Bhardwaj A, Konar P, Nayak AK (2020) Invisible Higgs search through vector boson fusion: a deep learning approach. Eur Phys J C. https:\/\/doi.org\/10.1140\/epjc\/s10052-020-08629-w","journal-title":"Eur Phys J C"},{"key":"148_CR23","doi-asserted-by":"crossref","unstructured":"Kasieczka G, Plehn T, Butter A, Cranmer K, Debnath D, Dillon BM, Fairbairn M, Faroughy DA, Fedorko W, Gay C, Gouskos L, Kamenik JF, Komiske P, Leiss S, Lister A, Macaluso S, Metodiev E, Moore L, Nachman B, Nordstr\u00f6m K, Pearkes J, Qu H, Rath Y, Rieger M, Shih D, Thompson J, Varma S (2019) The Machine Learning landscape of top taggers. SciPost Physics 7(1):4\u20133","DOI":"10.21468\/SciPostPhys.7.1.014"},{"key":"148_CR24","unstructured":"Kaggle: Rain in Australia (2020). https:\/\/www.kaggle.com\/datasets\/jsphyg\/weather-dataset-rattle-package"},{"issue":"6","key":"148_CR25","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1109\/MSP.2012.2211477","volume":"29","author":"L Deng","year":"2012","unstructured":"Deng L (2012) The MNIST database of handwritten digit images for machine learning research. IEEE Signal Process Mag 29(6):141\u2013142","journal-title":"IEEE Signal Process Mag"},{"key":"148_CR26","doi-asserted-by":"publisher","DOI":"10.3390\/s20082200","author":"D Garcia-Gonzalez","year":"2020","unstructured":"Garcia-Gonzalez D, Rivero D, Fernandez-Blanco E, Luaces MR (2020) A public domain dataset for real-life human activity recognition using smartphone sensors. Sensors. https:\/\/doi.org\/10.3390\/s20082200","journal-title":"Sensors"},{"key":"148_CR27","doi-asserted-by":"publisher","unstructured":"Johnson A, Bulgarelli L, Pollard T, Horng S, Celi LA, Mark R (2023) MIMIC-IV 2:2. https:\/\/doi.org\/10.13026\/6mm1-ek67","DOI":"10.13026\/6mm1-ek67"},{"issue":"1","key":"148_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-022-01899-x","volume":"10","author":"AEW Johnson","year":"2023","unstructured":"Johnson AEW, Bulgarelli L, Shen L, Gayles A, Shammout A, Horng S, Pollard TJ, Hao S, Moody B, Gow B, Lehman L.-w.H, Celi L.A., Mark R.G. (2023) MIMIC-IV, a freely accessible electronic health record dataset. Sci Data 10(1):1. https:\/\/doi.org\/10.1038\/s41597-022-01899-x","journal-title":"Sci Data"},{"issue":"23","key":"148_CR29","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1161\/01.CIR.101.23.e215","volume":"101","author":"AL Goldberger","year":"2000","unstructured":"Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):215\u2013220. https:\/\/doi.org\/10.1161\/01.CIR.101.23.e215","journal-title":"Circulation"},{"key":"148_CR30","unstructured":"Gupta M, Gallamoza B, Cutrona N, Dhakal P, Poulain R, Beheshti R (2022) An Extensive Data Processing Pipeline for MIMIC-IV. In: https:\/\/proceedings.mlr.press\/v193\/gupta22a.htmlProceedings of the 2nd Machine Learning for Health Symposium. Proceedings of Machine Learning Research, vol. 193, pp. 311\u2013325. PMLR"},{"issue":"2","key":"148_CR31","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/S0016-0032(96)00063-4","volume":"334","author":"ML Men\u00e9ndez","year":"1997","unstructured":"Men\u00e9ndez ML, Pardo JA, Pardo L, Pardo MC (1997) The Jensen-Shannon divergence. J Franklin Inst 334(2):307\u2013318. https:\/\/doi.org\/10.1016\/S0016-0032(96)00063-4","journal-title":"J Franklin Inst"},{"key":"148_CR32","doi-asserted-by":"publisher","unstructured":"Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J, van der Walt SJ, Brett M, Wilson J, Millman KJ, Mayorov N, Nelson ARJ, Jones E, Kern R, Larson E, Carey CJ, Polat \u0130, Feng Y, Moore EW, VanderPlas J, Laxalde D, Perktold J, Cimrman R, Henriksen I, Quintero EA, Harris CR, Archibald AM, Ribeiro AH, Pedregosa F, van Mulbregt P (2020) SciPy 1.0 Contributors: SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods 17:261\u2013272. https:\/\/doi.org\/10.1038\/s41592-019-0686-2","DOI":"10.1038\/s41592-019-0686-2"}],"container-title":["Computing and Software for Big Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41781-025-00148-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41781-025-00148-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41781-025-00148-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T17:35:42Z","timestamp":1762364142000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41781-025-00148-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,5]]},"references-count":32,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["148"],"URL":"https:\/\/doi.org\/10.1007\/s41781-025-00148-1","relation":{},"ISSN":["2510-2036","2510-2044"],"issn-type":[{"type":"print","value":"2510-2036"},{"type":"electronic","value":"2510-2044"}],"subject":[],"published":{"date-parts":[[2025,11,5]]},"assertion":[{"value":"31 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 November 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":"The authors declare no Competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"19"}}