{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:04:34Z","timestamp":1757617474442,"version":"3.44.0"},"reference-count":18,"publisher":"Springer Science and Business Media LLC","issue":"28","license":[{"start":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T00:00:00Z","timestamp":1737590400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T00:00:00Z","timestamp":1737590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100009411","name":"Universit\u00e9 Paris-Est Cr\u00e9teil","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100009411","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>In biomedical research and artificial intelligence, access to large, well-balanced, and representative datasets is crucial for developing trustworthy applications that can be used in real-world scenarios. However, obtaining such datasets can be challenging, as they are often restricted to hospitals and specialized facilities. To address this issue, the study proposes to generate highly realistic synthetic faces exhibiting drug abuse traits through augmentation. The proposed method, called \u201d3DG-GA\u201d, Deep De-identified anonymous Dataset Generation, uses a Genetic Algorithm as a strategy for synthetic face generation. The algorithm includes GAN-based artificial face generation, forgery detection, and face recognition. Initially, a dataset of 120 images of actual facial drug abuse is used. By preserving the drug traits, the 3DG-GA provides a dataset containing 3000 synthetic facial drug abuse images. The dataset will be open to the scientific community, allowing others to reproduce our results and benefit from the generated datasets while avoiding legal or ethical restrictions. Additionally, we validated the dataset by training a CNN model on the synthetic images and validating it on previously unseen real images. The model achieved an accuracy of 97.2% on the unseen real images, demonstrating the high quality and applicability of the synthetic data.<\/jats:p>","DOI":"10.1007\/s11042-025-20639-y","type":"journal-article","created":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T02:59:26Z","timestamp":1737601166000},"page":"34629-34643","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Generation of artificial facial drug abuse images using deep de-identified anonymous dataset augmentation through genetics algorithm (3DG-GA)"],"prefix":"10.1007","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3015-847X","authenticated-orcid":false,"given":"Hazem","family":"Zein","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lou","family":"Laurent","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R\u00e9gis","family":"Fournier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amine","family":"Nait-Ali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,23]]},"reference":[{"key":"20639_CR1","doi-asserted-by":"crossref","unstructured":"Karras T, Laine S, Aittala M, Hellsten J, Lehtinen J, Aila T (2020) Analyzing and Improving the Image Quality of StyleGAN. arXiv. arXiv:1912.04958 [cs, eess, stat] .","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"20639_CR2","doi-asserted-by":"publisher","unstructured":"Arita Y, Akita H, Fujiwara H, Hashimoto M, Shigeta K, Kwee TC, Yoshida S, Kosaka T, Okuda S, Oya M, Jinzaki M (2022) Synthetic magnetic resonance imaging for primary prostate cancer evaluation: Diagnostic potential of a non-contrast-enhanced bi-parametric approach enhanced with relaxometry measurements. European J Radio Open 9, 100403 . https:\/\/doi.org\/10.1016\/j.ejro.2022.100403","DOI":"10.1016\/j.ejro.2022.100403"},{"key":"20639_CR3","doi-asserted-by":"publisher","unstructured":"Frid-Adar M, Diamant I, Klang E, Amitai M, Goldberger J, Greenspan H (2018) GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomput 321:321\u2013331 . https:\/\/doi.org\/10.1016\/j.neucom.2018.09.013","DOI":"10.1016\/j.neucom.2018.09.013"},{"key":"20639_CR4","doi-asserted-by":"publisher","unstructured":"Chen JS, Coyner AS, Chan RVP, Hartnett ME, Moshfeghi DM, Owen LA, Kalpathy-Cramer J, Chiang MF, Campbell JP (2021) Deepfakes in Ophthalmology. Ophthalmology. Sci 1(4):100079. https:\/\/doi.org\/10.1016\/j.xops.2021.100079","DOI":"10.1016\/j.xops.2021.100079"},{"key":"20639_CR5","doi-asserted-by":"publisher","unstructured":"Coyner AS, Chen JS, Chang K, Singh P, Ostmo S, Chan RVP, Chiang MF, Kalpathy-Cramer J, Campbell JP (2022) Synthetic Medical Images for Robust, Privacy-Preserving Training of Artificial Intelligence. Ophthalmol Sci 2(2):100126. https:\/\/doi.org\/10.1016\/j.xops.2022.100126","DOI":"10.1016\/j.xops.2022.100126"},{"key":"20639_CR6","doi-asserted-by":"publisher","unstructured":"Zein H, Chantaf S, El-Saleh R, Nait-Ali A (2021) Generative Adversarial Networks Based Approach for Artificial Face Dataset Generation in Acne Disease Cases. In: 2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART), pp 1\u20134 . https:\/\/doi.org\/10.1109\/BioSMART54244.2021.9677572","DOI":"10.1109\/BioSMART54244.2021.9677572"},{"key":"20639_CR7","unstructured":"M C S Office. Faces of Meth - Before & After Meth Use. https:\/\/facesofmeth.us\/"},{"key":"20639_CR8","unstructured":"National Institute on Drug Abuse: Methamphetamine DrugFacts (2019). https:\/\/nida.nih.gov\/publications\/drugfacts\/methamphetamine. Accessed: 2023-5-24"},{"issue":"2","key":"20639_CR9","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/S0740-5472(02)00267-2","volume":"23","author":"TE Freese","year":"2002","unstructured":"Freese TE, Miotto K, Reback CJ (2002) The effects and consequences of selected club drugs. J Subs Abuse Treat 23(2):151\u2013156. https:\/\/doi.org\/10.1016\/S0740-5472(02)00267-2","journal-title":"J Subs Abuse Treat"},{"key":"20639_CR10","doi-asserted-by":"publisher","unstructured":"Vearrier D, Greenberg MI, Miller SN, Okaneku JT, Haggerty DA (2012) Methamphetamine: History, pathophysiology, adverse health effects, current trends, and hazards associated with the clandestine manufacture of methamphetamine. Methamphetamine: History, Pathophysiology, Adverse Health Effects, Current Trends, and Hazards Associated with the Clandestine Manufacture of Methamphetamine. Disease-a-Month 58(2):38\u201389 . https:\/\/doi.org\/10.1016\/j.disamonth.2011.09.004.","DOI":"10.1016\/j.disamonth.2011.09.004"},{"key":"20639_CR11","doi-asserted-by":"publisher","unstructured":"Kazemi V, Sullivan J (2014) One millisecond face alignment with an ensemble of regression trees. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp 1867\u20131874. IEEE, Columbus, OH . https:\/\/doi.org\/10.1109\/CVPR.2014.241. https:\/\/ieeexplore.ieee.org\/document\/6909637","DOI":"10.1109\/CVPR.2014.241"},{"key":"20639_CR12","doi-asserted-by":"publisher","unstructured":"Harastani M, Benterkia A, Zadeh FM, Nait-Ali A (2020) Methamphetamine drug abuse and addiction: Effects on face asymmetry. Comput Bio Med 116:103475 . https:\/\/doi.org\/10.1016\/j.compbiomed.2019.103475","DOI":"10.1016\/j.compbiomed.2019.103475"},{"key":"20639_CR13","doi-asserted-by":"crossref","unstructured":"Li X, Chen C, Zhou S, Lin X, Zuo W, Zhang L (2020) Blind face restoration via deep multi-scale component dictionaries. In: ECCV","DOI":"10.1007\/978-3-030-58545-7_23"},{"key":"20639_CR14","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/3927.001.0001","volume-title":"An Introduction to Genetic Algorithms","author":"M Mitchell","year":"1996","unstructured":"Mitchell M (1996) An Introduction to Genetic Algorithms. Complex adaptive systems. MIT Press, Cambridge, Mass"},{"key":"20639_CR15","doi-asserted-by":"publisher","unstructured":"Chollet F (2016) Xception: Deep Learning with Depthwise Separable Convolutions. https:\/\/doi.org\/10.48550\/ARXIV.1610.02357","DOI":"10.48550\/ARXIV.1610.02357"},{"key":"20639_CR16","doi-asserted-by":"publisher","unstructured":"Rossler A, Cozzolino D, Verdoliva L, Riess C, Thies J, Niessner M (2019) FaceForensics++: Learning to Detect Manipulated Facial Images. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp 1\u201311. IEEE, Seoul, Korea (South) . https:\/\/doi.org\/10.1109\/ICCV.2019.00009. https:\/\/ieeexplore.ieee.org\/document\/9010912\/","DOI":"10.1109\/ICCV.2019.00009"},{"key":"20639_CR17","doi-asserted-by":"publisher","unstructured":"Wang Z, Simoncelli EP, Bovik AC (2003) Multiscale structural similarity for image quality assessment. In: The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, pp 1398\u20131402. IEEE, Pacific Grove, CA, USA. https:\/\/doi.org\/10.1109\/ACSSC.2003.1292216. http:\/\/ieeexplore.ieee.org\/document\/1292216\/","DOI":"10.1109\/ACSSC.2003.1292216"},{"key":"20639_CR18","doi-asserted-by":"publisher","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. Proceed AAAI Conf Art Intell 31(1) https:\/\/doi.org\/10.1609\/aaai.v31i1.11231","DOI":"10.1609\/aaai.v31i1.11231"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-025-20639-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-025-20639-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-025-20639-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T04:25:39Z","timestamp":1757132739000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-025-20639-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,23]]},"references-count":18,"journal-issue":{"issue":"28","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["20639"],"URL":"https:\/\/doi.org\/10.1007\/s11042-025-20639-y","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2025,1,23]]},"assertion":[{"value":"18 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 January 2025","order":4,"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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interests\/Competing Interest"}}]}}