{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T16:00:29Z","timestamp":1780588829604,"version":"3.54.1"},"reference-count":99,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T00:00:00Z","timestamp":1750118400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T00:00:00Z","timestamp":1750118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>The manipulation of digital images has become a popular trend. Due to the development of image processing tools and visualization techniques integrating deep learning and artificial intelligence (AI) algorithms (in particular generative adversarial networks\u2013GAN), this can pose serious threats to privacy and security. In recent years, Deepfake algorithms have been designed to exchange faces or modify facial features, potentially leading to more severe problems in this context. In this manuscript, we provide a comprehensive review of the two most important facial image processing technologies: (i) deepfake face manipulation; and (ii) face manipulation detection techniques. Furthermore, we explore the state-of-the-art of popular GAN techniques. In particular, three types of Deepfake face detection technologies are reviewed: (i) hand-crafted features (ii) artifacts; and (iii) learning-based features, while highlighting related improvements and challenges. Furthermore, this article discusses potential challenges and promising research directions for future investigation. We believe that this review has been organized to provide a structured analysis of important research papers and to discuss each study\u2019s main findings and conclusions. Our investigation reveals shortcomings in manipulation detection benchmarks due to real-world scenario variations and biased dataset comparisons. Current research priorities revolve around enhancing GAN training stability, resolution, and manipulable facial features. Moreover, GANs have shown superior results in identifying fake images; however, their reliance prompts a systematic approach to detecting fakes. This dependency raises questions about detecting fake images with or without manipulated GAN architecture, urging the need for novel computational techniques to identify manipulations without GAN assistance.<\/jats:p>","DOI":"10.1007\/s44163-025-00337-2","type":"journal-article","created":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T10:42:58Z","timestamp":1750156978000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A comparative study of deepfake facial manipulation technique using generative adversarial networks"],"prefix":"10.1007","volume":"5","author":[{"given":"Wasin","family":"Al Kishri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jabar H.","family":"Yousif","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahmood","family":"Al Bahri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Zakarya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naveed","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanad Sulaiman","family":"Al Maskari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmet","family":"Gurhanli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,17]]},"reference":[{"key":"337_CR1","doi-asserted-by":"publisher","unstructured":"R\"ossler A, Cozzolino D, Verdoliva L, Riess C, Thies J, Niessner M. Faceforensics++: learning to detect manipulated facial images. In: Proceedings of the IEEE international conference on computer vision. 2019. p. 1\u20139. https:\/\/doi.org\/10.1109\/ICCV.2019.00009. Accessed 20 May 2022.","DOI":"10.1109\/ICCV.2019.00009"},{"key":"337_CR2","doi-asserted-by":"publisher","unstructured":"Gamage D, Ghasiya P, Bonagiri V, Whiting M, Sasahara K. Are deepfakes concerning? analyzing conversations of deepfakes on reddit and exploring societal implications. In: Proceedings of the ACM. 2022. https:\/\/doi.org\/10.1145\/3491102.3517446.","DOI":"10.1145\/3491102.3517446"},{"key":"337_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.dsp.2017.08.009","volume":"71","author":"P Korus","year":"2017","unstructured":"Korus P. Digital image integrity a survey of protection and verification techniques. Digit Signal Process. 2017;71:1\u201326. https:\/\/doi.org\/10.1016\/j.dsp.2017.08.009.","journal-title":"Digit Signal Process"},{"key":"337_CR4","doi-asserted-by":"publisher","unstructured":"Galbally J, Marcel S. Face anti-spoofing based on general image quality assessment. In: Proceedings of the international conference on pattern recognition. 2014. p. 1173\u201378. https:\/\/doi.org\/10.1109\/ICPR.2014.211. https:\/\/ieeexplore.ieee.org\/abstract\/document\/6976921. Accessed 03 Oct 2021.","DOI":"10.1109\/ICPR.2014.211"},{"key":"337_CR5","doi-asserted-by":"publisher","unstructured":"Marcel S, Nixon MS, Fierrez J, Evans N, editors. Handbook of biometric anti-spoofing. Springer; 2019. https:\/\/doi.org\/10.1007\/978-3-319-92627-8.","DOI":"10.1007\/978-3-319-92627-8"},{"issue":"5","key":"337_CR6","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1109\/JSTSP.2020.3007250","volume":"14","author":"JC Neves","year":"2020","unstructured":"Neves JC, Tolosana R, Vera-Rodriguez R, Lopes V, Proen\u00e7a H, Fierrez J. Ganprintr: improved fakes and evaluation of the state of the art in face manipulation detection. IEEE J Sel Top Signal Process. 2020;14(5):1038\u201348. https:\/\/doi.org\/10.1109\/JSTSP.2020.3007250.","journal-title":"IEEE J Sel Top Signal Process"},{"key":"337_CR7","doi-asserted-by":"crossref","unstructured":"Dang H, Liu F, Stehouwer J, Liu X, Jain AK. On the detection of digital face manipulation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2020. https:\/\/openaccess.thecvf.com\/content_CVPR_2020\/html\/Dang_On_the_Detection_of_Digital_Face_Manipulation_CVPR_2020_paper.html. Accessed 19 Aug 2022.","DOI":"10.1109\/CVPR42600.2020.00582"},{"issue":"1535","key":"337_CR8","doi-asserted-by":"publisher","first-page":"3453","DOI":"10.1098\/rstb.2009.0142","volume":"364","author":"C Frith","year":"2009","unstructured":"Frith C. Role of facial expressions in social interactions. Philos Trans R Soc B Biol Sci. 2009;364(1535):3453\u20138. https:\/\/doi.org\/10.1098\/rstb.2009.0142.","journal-title":"Philos Trans R Soc B Biol Sci"},{"issue":"2","key":"337_CR9","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1111\/cgf.13382","volume":"37","author":"M Zollh\u00f6fer","year":"2018","unstructured":"Zollh\u00f6fer M, Thies J, Garrido P, Bradley D, Beeler T, P\u00e9rez P, Stamminger M, Nie\u00dfner M, Theobalt C. State of the art on monocular 3d face reconstruction, tracking, and applications. Comput Graph Forum. 2018;37(2):523\u201350. https:\/\/doi.org\/10.1111\/cgf.13382.","journal-title":"Comput Graph Forum"},{"key":"337_CR10","unstructured":"Tan M, Le Q. Efficientnet: rethinking model scaling for convolutional neural networks. In: Proceedings of the international conference on machine learning. 2019. http:\/\/proceedings.mlr.press\/v97\/tan19a.html."},{"issue":"7","key":"337_CR11","doi-asserted-by":"publisher","first-page":"1678","DOI":"10.1007\/s11263-022-01606-8","volume":"130","author":"F Juefei-Xu","year":"2022","unstructured":"Juefei-Xu F, Wang R, Huang Y, Guo Q, Ma L, Liu Y. Countering malicious deepfakes: survey, battleground, and horizon. Int J Comput Vis. 2022;130(7):1678\u2013734. https:\/\/doi.org\/10.1007\/s11263-022-01606-8.","journal-title":"Int J Comput Vis"},{"key":"337_CR12","doi-asserted-by":"publisher","unstructured":"Afchar D, Nozick V, Yamagishi J, Echizen I. Mesonet: a compact facial video forgery detection network. In: 2018 IEEE international workshop on information forensics and security (WIFS). 2018. https:\/\/doi.org\/10.1109\/wifs.2018.8630761.","DOI":"10.1109\/wifs.2018.8630761"},{"key":"337_CR13","doi-asserted-by":"publisher","unstructured":"Jain A, Singh, R, Vatsa M. On detecting gans and retouching based synthetic alterations. In: Proceedings of the IEEE international workshop on biometrics and forensics (BTAS). 2018. https:\/\/doi.org\/10.1109\/BTAS.2018.8698545. https:\/\/ieeexplore.ieee.org\/document\/8698545. Accessed: 19 Aug 2022.","DOI":"10.1109\/BTAS.2018.8698545"},{"key":"337_CR14","doi-asserted-by":"publisher","unstructured":"Zhang X, Karaman S, Chang S-F. Detecting and simulating artifacts in gan fake images. In: 2019 IEEE international workshop on information forensics and security (WIFS). 2019. https:\/\/doi.org\/10.1109\/wifs47025.2019.9035107.","DOI":"10.1109\/wifs47025.2019.9035107"},{"key":"337_CR15","doi-asserted-by":"crossref","unstructured":"Fernandes S, Raj S, Ewetz R, Pannu JS, Jha SK, Ortiz E, Salter M. Detecting deepfake videos using attribution-based confidence metric. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops. 2020. p. 308\u201309.","DOI":"10.1109\/CVPRW50498.2020.00162"},{"key":"337_CR16","doi-asserted-by":"crossref","unstructured":"Theerthagiri P, Basha\u00a0Nagaladinne G. Deepfake face detection using deep inceptionnet learning algorithm. In: 2023 IEEE international students\u2019 conference on electrical, electronics and computer science (SCEECS). 2023. p. 1\u20136.","DOI":"10.1109\/SCEECS57921.2023.10063128"},{"issue":"9","key":"337_CR17","doi-asserted-by":"publisher","first-page":"2998","DOI":"10.3390\/s21092998","volume":"21","author":"A Khan","year":"2021","unstructured":"Khan A, Jin W, Haider A, Rahman M, Wang D. Adversarial gaussian denoiser for multiple-level image denoising. Sensors. 2021;21(9):2998.","journal-title":"Sensors"},{"key":"337_CR18","doi-asserted-by":"publisher","DOI":"10.1080\/23268743.2020.1757499","author":"S Maddocks","year":"2020","unstructured":"Maddocks S. \u201cA deepfake porn plot intended to silence me\": exploring continuities between pornographic and \u201cpolitical\" deep fakes. Porn Stud. 2020. https:\/\/doi.org\/10.1080\/23268743.2020.1757499.","journal-title":"Porn Stud"},{"key":"337_CR19","doi-asserted-by":"crossref","unstructured":"Isola P, Zhu J-Y, Zhou T, Efros AA. Image-to-image translation with conditional adversarial networks. In: CVPR. 2017. https:\/\/openaccess.thecvf.com\/content_cvpr_2017\/html\/Isola_Image-To-Image_Translation_With_CVPR_2017_paper.html.","DOI":"10.1109\/CVPR.2017.632"},{"key":"337_CR20","doi-asserted-by":"crossref","unstructured":"Liu M-Y, Huang X, Mallya A, Karras T, Aila T, Lehtinen J, Kautz J. Few-shot unsupervised image-to-image translation. In: ICCV. 2019. https:\/\/openaccess.thecvf.com\/content_ICCV_2019\/html\/Liu_Few-Shot_Unsupervised_Image-to-Image_Translation_ICCV_2019_paper.html. Accessed 19 Oct 2022.","DOI":"10.1109\/ICCV.2019.01065"},{"key":"337_CR21","doi-asserted-by":"publisher","unstructured":"He P, Li H, Wang H. Detection of fake images via the ensemble of deep representations from multi color spaces. In: ICIP. 2019. https:\/\/doi.org\/10.1109\/ICIP.2019.8803740. https:\/\/ieeexplore.ieee.org\/abstract\/document\/8803740. Accessed 17 Apr 2022.","DOI":"10.1109\/ICIP.2019.8803740"},{"key":"337_CR22","doi-asserted-by":"crossref","unstructured":"Xiao C, Li B, Zhu J-Y, He W, Liu M, Song D. Generating adversarial examples with adversarial networks. 2019. arXiv:1801.02610.","DOI":"10.24963\/ijcai.2018\/543"},{"issue":"4","key":"337_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3306346.3323035","volume":"38","author":"J Thies","year":"2019","unstructured":"Thies J, Zollhfer M, Niener M. Deferred neural rendering. ACM Trans Graph. 2019;38(4):1\u201312. https:\/\/doi.org\/10.1145\/3306346.3323035.","journal-title":"ACM Trans Graph"},{"issue":"8","key":"337_CR24","doi-asserted-by":"publisher","first-page":"2001","DOI":"10.1109\/TIFS.2018.2807791","volume":"13","author":"E Gonzalez-Sosa","year":"2018","unstructured":"Gonzalez-Sosa E, Fierrez J, Vera-Rodriguez R, Alonso-Fernandez F. Facial soft biometrics for recognition in the wild: recent works, annotation, and cots evaluation. IEEE Trans Inf Forensics Secur. 2018;13(8):2001\u201314. https:\/\/doi.org\/10.1109\/TIFS.2018.2807791.","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"337_CR25","doi-asserted-by":"crossref","unstructured":"Choi Y, Choi M, Kim M, Ha J-W, Kim S, Choo J. Stargan: unified generative adversarial networks for multi-domain image-to-image translation. In: CVPR. 2018. https:\/\/openaccess.thecvf.com\/content_cvpr_2018\/html\/Choi_StarGAN_Unified_Generative_CVPR_2018_paper.html.","DOI":"10.1109\/CVPR.2018.00916"},{"key":"337_CR26","doi-asserted-by":"crossref","unstructured":"Karras T, Laine S, Aila T. A style-based generator architecture for generative adversarial networks. In: CVPR. 2019. https:\/\/openaccess.thecvf.com\/content_CVPR_2019\/html\/Karras_A_Style-Based_Generator_Architecture_for_Generative_Adversarial_Networks_CVPR_2019_paper.html.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"337_CR27","doi-asserted-by":"crossref","unstructured":"Sharma SK, AlEnizi A, Kumar M, Alfarraj O, Alowaidi M. Detection of real-time deep fakes and face forgery in video conferencing employing generative adversarial networks. Heliyon. 2024;10(17).","DOI":"10.1016\/j.heliyon.2024.e37163"},{"key":"337_CR28","unstructured":"Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial networks. 2014. https:\/\/arxiv.org\/abs\/1406.2661."},{"issue":"1","key":"337_CR29","first-page":"9","volume":"16","author":"M Wyawahare","year":"2025","unstructured":"Wyawahare M, Bhorge S, Rane M, Parkhi V, Jha M, Muhal N. Comparative analysis of deepfake detection models on diverse gan-generated images. Int J Electr Comput Eng Syst. 2025;16(1):9\u201318.","journal-title":"Int J Electr Comput Eng Syst"},{"key":"337_CR30","unstructured":"Ma X, Wang Y, Houle ME, Zhou S, Erfani S, Xia S, Wijewickrema S, Bailey J. Dimensionality-driven learning with noisy labels. In: Proceedings of the 80th annual meeting of the association for computational linguistics. 2018. https:\/\/proceedings.mlr.press\/v80\/ma18d.html. Accessed 19 Aug 2022."},{"issue":"1","key":"337_CR31","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/msp.2017.2765202","volume":"35","author":"A Creswell","year":"2018","unstructured":"Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AA. Generative adversarial networks: an overview. IEEE Signal Process Mag. 2018;35(1):53\u201365. https:\/\/doi.org\/10.1109\/msp.2017.2765202.","journal-title":"IEEE Signal Process Mag"},{"key":"337_CR32","doi-asserted-by":"crossref","unstructured":"Goetschalckx L, Andonian A, Oliva A, Isola P. Ganalyze: toward visual definitions of cognitive image properties. In: Proceedings of the IEEE\/CVF international conference on computer vision. 2019. https:\/\/openaccess.thecvf.com\/content_ICCV_2019\/html\/Goetschalckx_GANalyze_Toward_Visual_Definitions_of_Cognitive_Image_Properties_ICCV_2019_paper.html. Accessed 19 Aug 2022.","DOI":"10.1109\/ICCV.2019.00584"},{"key":"337_CR33","doi-asserted-by":"publisher","unstructured":"Yang X, Li Y, Qi H, Lyu S. Exposing gan-synthesized faces using landmark locations. In: Proceedings of the ACM workshop on information hiding and multimedia security. 2019. https:\/\/doi.org\/10.1145\/3335203.3335724.","DOI":"10.1145\/3335203.3335724"},{"key":"337_CR34","unstructured":"Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville A. Improved training of wasserstein gans. 2017. arXiv:1704.00028 [cs, stat]."},{"key":"337_CR35","unstructured":"Petzka H, Fischer A, Lukovnicov D. On the regularization of Wasserstein GANs. 2018. arXiv:1709.08894 [cs, stat]."},{"key":"337_CR36","doi-asserted-by":"publisher","unstructured":"Ledig C, Theis L, Huszar F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z, Shi W. Photo-realistic single image super-resolution using a generative adversarial network. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). 2017. https:\/\/doi.org\/10.1109\/cvpr.2017.19.","DOI":"10.1109\/cvpr.2017.19"},{"key":"337_CR37","doi-asserted-by":"publisher","unstructured":"Zhu J-Y, Park T, Isola P, Efros AA. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: 2017 IEEE international conference on computer vision (ICCV). 2017. https:\/\/doi.org\/10.1109\/iccv.2017.244.","DOI":"10.1109\/iccv.2017.244"},{"issue":"5","key":"337_CR38","doi-asserted-by":"publisher","first-page":"532","DOI":"10.2352\/issn.2470-1173.2019.5.mwsf-532","volume":"2019","author":"L Nataraj","year":"2019","unstructured":"Nataraj L, Mohammed TM, Manjunath BS, Chandrasekaran S, Flenner A, Bappy JH, Roy-Chowdhury AK. Detecting gan generated fake images using co-occurrence matrices. Electron Imaging. 2019;2019(5):532\u20137. https:\/\/doi.org\/10.2352\/issn.2470-1173.2019.5.mwsf-532.","journal-title":"Electron Imaging"},{"key":"337_CR39","doi-asserted-by":"crossref","unstructured":"Choi Y, Uh Y, Yoo J, Ha J-W. StarGAN V2: diverse image synthesis for multiple domains. cs. 2020.","DOI":"10.1109\/CVPR42600.2020.00821"},{"key":"337_CR40","doi-asserted-by":"crossref","unstructured":"Yang C, Lu X, Lin Z, Shechtman E, Wang O, Li H. High-resolution image inpainting using multi-scale neural patch synthesis. cs. 2017.","DOI":"10.1109\/CVPR.2017.434"},{"key":"337_CR41","doi-asserted-by":"crossref","unstructured":"Lee C-H, Liu Z, Wu L, Luo P. MaskGAN: towards diverse and interactive facial image manipulation. 2020. https:\/\/openaccess.thecvf.com\/content_CVPR_2020\/html\/Lee_MaskGAN_Towards_Diverse_and_Interactive_Facial_Image_Manipulation_CVPR_2020_paper.html. Accessed 19 Aug 2022.","DOI":"10.1109\/CVPR42600.2020.00559"},{"key":"337_CR42","unstructured":"Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks. 2015."},{"key":"337_CR43","unstructured":"Perarnau G, Weijer J, Raducanu B, lvarez JM. Invertible conditional GANs for image editing. cs. 2016."},{"key":"337_CR44","doi-asserted-by":"crossref","unstructured":"Ding H, Sricharan K, Chellappa R. Exprgan: facial expression editing with controllable expression intensity. In: Proceedings of the AAAI conference on artificial intelligence. 2018. p. 32.","DOI":"10.1609\/aaai.v32i1.12277"},{"key":"337_CR45","unstructured":"Arjovsky M, Chintala S, Bottou L. Wasserstein GAN. cs, stat. 2017."},{"key":"337_CR46","unstructured":"Brock A, Donahue J, Simonyan K. Large scale gan training for high fidelity natural image synthesis. 2018. arXiv:1809.11096."},{"key":"337_CR47","doi-asserted-by":"publisher","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L. Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. 2009. https:\/\/doi.org\/10.1109\/cvpr.2009.5206848.","DOI":"10.1109\/cvpr.2009.5206848"},{"key":"337_CR48","doi-asserted-by":"publisher","unstructured":"Zhang G, Kan M, Shan S, Chen X. Generative adversarial network with spatial attention for face attribute editing. In: Proceedings of the 2020 international conference on computer vision. 2020. https:\/\/doi.org\/10.1007\/978-3-030-01231-1_26. https:\/\/www.semanticscholar.org\/paper\/Generative-Adversarial-Network-with-Spatial-for-Zhang-Kan\/21d1315761131ea6b3e2afe7a745b606341616fd.","DOI":"10.1007\/978-3-030-01231-1_26"},{"key":"337_CR49","doi-asserted-by":"crossref","unstructured":"He Z, Zuo W, Kan M, Shan S, Chen X. Attgan: facial attribute editing by only changing what you want2018. arXiv:1711.10678 [cs, stat].","DOI":"10.1109\/TIP.2019.2916751"},{"key":"337_CR50","doi-asserted-by":"crossref","unstructured":"Wang R, Juefei-Xu F, Ma L, Xie X, Huang Y, Wang J, Liu Y. Fakespotter: a simple yet robust baseline for spotting ai-synthesized fake faces. 2020. arXiv:1909.06122 [cs].","DOI":"10.24963\/ijcai.2020\/476"},{"key":"337_CR51","unstructured":"Karras T, Aila T, Laine S, Lehtinen J. Progressive growing of gans for improved quality, stability, and variation. 2017. arXiv:1710.10196."},{"key":"337_CR52","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2022.3155571","author":"Y Nirkin","year":"2022","unstructured":"Nirkin Y, Hassner T, Keller Y. Fsganv2: better subject agnostic face swapping and reenactment. IEEE Trans Pattern Anal Mach Intell. 2022. https:\/\/doi.org\/10.1109\/tpami.2022.3155571.","journal-title":"IEEE Trans Pattern Anal Mach Intell."},{"issue":"1","key":"337_CR53","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1007\/s44163-024-00138-z","volume":"4","author":"J Jenkins","year":"2024","unstructured":"Jenkins J, Roy K. Exploring deep convolutional generative adversarial networks (dcgan) in biometric systems: a survey study. Discov Artif Intell. 2024;4(1):42.","journal-title":"Discov Artif Intell"},{"key":"337_CR54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00297","author":"G Yang","year":"2021","unstructured":"Yang G, Fei N, Ding M, Liu G, Lu Z, Xiang T. L2m-gan: learning to manipulate latent space semantics for facial attribute editing. Proc IEEE Conf Comput Vis Pattern Recognit. 2021. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00297.","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"337_CR55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01068","author":"S Hyun","year":"2021","unstructured":"Hyun S, Kim J, Heo J-P. Self-supervised video gans: learning for appearance consistency and motion coherency. Proc IEEE Conf Comput Vis Pattern Recognit. 2021. https:\/\/doi.org\/10.1109\/CVPR46437.2021.01068.","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"337_CR56","unstructured":"Yi D, Lei Z, Liao S, Li SZ. Learning face representation from scratch. 2014. www.arxiv-vanity.com."},{"key":"337_CR57","doi-asserted-by":"publisher","unstructured":"Liu Z, Luo P, Wang X, Tang X. Deep learning face attributes in the wild. In: Proceedings of the IEEE international conference on computer vision (ICCV). 2015. https:\/\/doi.org\/10.1109\/iccv.2015.425.","DOI":"10.1109\/iccv.2015.425"},{"key":"337_CR58","doi-asserted-by":"publisher","unstructured":"Sun Y, Wang X, Tang X. Hybrid deep learning for face verification. In: IEEE conference on computer vision and pattern recognition (ICCV). 2013. https:\/\/doi.org\/10.1109\/ICCV.2013.188. Accessed 19 Aug 2022.","DOI":"10.1109\/ICCV.2013.188"},{"key":"337_CR59","doi-asserted-by":"crossref","unstructured":"Parkhi OM, Vedaldi A, Zisserman A. Deep face recognition. 2015. https:\/\/ora.ox.ac.uk.","DOI":"10.5244\/C.29.41"},{"key":"337_CR60","doi-asserted-by":"publisher","unstructured":"Kemelmacher-Shlizerman I, Seitz SM, Miller D, Brossard E. The megaface benchmark: 1 million faces for recognition at scale. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016. https:\/\/doi.org\/10.1109\/CVPR.2016.527. Accessed 19 Aug 2022.","DOI":"10.1109\/CVPR.2016.527"},{"key":"337_CR61","doi-asserted-by":"publisher","unstructured":"Cao Q, Shen L, Xie W, Parkhi OM, Zisserman A. Vggface2: a dataset for recognising faces across pose and age. In: 13th IEEE international conference on automatic face & gesture recognition (FG 2018). 2018. https:\/\/doi.org\/10.1109\/fg.2018.00020.","DOI":"10.1109\/fg.2018.00020"},{"key":"337_CR62","unstructured":"Korshunov P, Marcel S. Deepfakes: a new threat to face recognition? Assessment and detection. 2018. arXiv:1812.08685 [cs]."},{"key":"337_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2020.107616\/","volume":"174","author":"H Li","year":"2020","unstructured":"Li H, Li B, Tan S, Huang J. Identification of deep network generated images using disparities in color components. Signal Process. 2020;174: 107616. https:\/\/doi.org\/10.1016\/j.sigpro.2020.107616\/.","journal-title":"Signal Process"},{"key":"337_CR64","unstructured":"FaceApp. AI Face Editor. 2020. https:\/\/www.faceapp.com\/."},{"key":"337_CR65","unstructured":"Dufour N, Gully A. Contributing data to deepfake detection research. 2019."},{"key":"337_CR66","doi-asserted-by":"publisher","unstructured":"Dang H, Liu F, Stehouwer J, Liu X, Jain AK. On the detection of digital face manipulation. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR). 2020. https:\/\/doi.org\/10.1109\/cvpr42600.2020.00582.","DOI":"10.1109\/cvpr42600.2020.00582"},{"key":"337_CR67","doi-asserted-by":"publisher","unstructured":"Choi Y, Choi M, Kim M, Ha J-W, Kim S, Choo J. Stargan: unified generative adversarial networks for multi-domain image-to-image translation. In: IEEE\/CVF conference on computer vision and pattern recognition. 2018. https:\/\/doi.org\/10.1109\/cvpr.2018.00916.","DOI":"10.1109\/cvpr.2018.00916"},{"key":"337_CR68","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2023.109024","volume":"113","author":"H Byeon","year":"2024","unstructured":"Byeon H, Shabaz M, Shrivastava K, Joshi A, Keshta I, Oak R, Singh PP, Soni M. Deep learning model to detect deceptive generative adversarial network generated images using multimedia forensic. Comput Electr Eng. 2024;113: 109024.","journal-title":"Comput Electr Eng"},{"key":"337_CR69","unstructured":"Dolhansky B, Bitton J, Pflaum B, Lu J, Howes R, Wang M, Ferrer CC. The deepfake detection challenge (dfdc) dataset. 2020. arXiv:2006.07397 [cs]."},{"key":"337_CR70","doi-asserted-by":"publisher","unstructured":"He Y, Gan B, Chen S, Zhou Y, Yin G, Song L, Sheng L, Shao J, Liu Z. Forgerynet: a versatile benchmark for comprehensive forgery analysis. IEEE Trans Comput Vis Pattern Recognit 2021;46437:434. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00434. Accessed 19 Aug 2022.","DOI":"10.1109\/CVPR46437.2021.00434"},{"key":"337_CR71","doi-asserted-by":"crossref","unstructured":"Le T-N, Nguyen HH, Yamagishi J, Echizen I. Openforensics: large-scale challenging dataset for multi-face forgery detection and segmentation in-the-wild. 2021. arXiv:2107.14480 [cs].","DOI":"10.1109\/ICCV48922.2021.00996"},{"key":"337_CR72","unstructured":"Robinson JP. Automatic face understanding: recognizing families in photos. PhD thesis, ProQuest. 2020. https:\/\/doi.org\/28314324. https:\/\/www.proquest.com\/openview\/c7cc950b2e62621407f0278775665cf8\/1?pq-origsite=gscholar&cbl=18750 &diss=y. Accessed 19 Aug 2022."},{"issue":"1","key":"337_CR73","doi-asserted-by":"publisher","first-page":"10321","DOI":"10.2196\/10321","volume":"7","author":"P Agarwal","year":"2019","unstructured":"Agarwal P, Mukerji G, Desveaux L, Ivers NM, Bhattacharyya O, Hensel JM, Shaw J, Bouck Z, Jamieson T, Onabajo N, Cooper M, Marani H, Jeffs L, Bhatia RS. Mobile app for improved self-management of type 2 diabetes: multicenter pragmatic randomized controlled trial. JMIR Mhealth Uhealth. 2019;7(1):10321. https:\/\/doi.org\/10.2196\/10321.","journal-title":"JMIR Mhealth Uhealth"},{"key":"337_CR74","doi-asserted-by":"publisher","first-page":"83144","DOI":"10.1109\/access.2020.2988660","volume":"8","author":"T Jung","year":"2020","unstructured":"Jung T, Kim S, Kim K. Deepvision: deepfakes detection using human eye blinking pattern. IEEE Access. 2020;8:83144\u201354. https:\/\/doi.org\/10.1109\/access.2020.2988660.","journal-title":"IEEE Access"},{"key":"337_CR75","doi-asserted-by":"publisher","unstructured":"Wang Y, Dantcheva A. A video is worth more than 1000 lies. Comparing 3dcnn approaches for detecting deepfakes. In: IEEE conference on computer vision and pattern recognition. 2020. https:\/\/doi.org\/10.1109\/FG47880.2020.00089. https:\/\/ieeexplore.ieee.org\/document\/9320165. Accessed 19 Aug 2022.","DOI":"10.1109\/FG47880.2020.00089"},{"key":"337_CR76","doi-asserted-by":"publisher","unstructured":"Sun D, Yang X, Liu M-Y, Kautz J. Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume. In: 2018 IEEE\/CVF conference on computer vision and pattern recognition. 2018. https:\/\/doi.org\/10.1109\/cvpr.2018.00931.","DOI":"10.1109\/cvpr.2018.00931"},{"key":"337_CR77","doi-asserted-by":"publisher","unstructured":"Atoum Y, Liu Y, Jourabloo A, Liu X. Face anti-spoofing using patch and depth-based cnns. In: IEEE international conference on biometrics and security technologies. 2017. https:\/\/doi.org\/10.1109\/BTAS.2017.8272713. https:\/\/ieeexplore.ieee.org\/document\/8272713. Accessed 19 Aug 2022.","DOI":"10.1109\/BTAS.2017.8272713"},{"key":"337_CR78","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1117\/12.640109","volume":"6072","author":"J Luk","year":"2006","unstructured":"Luk J, Fridrich J, Goljan M. Detecting digital image forgeries using sensor pattern noise. NASA ADS. 2006;6072:362\u201372. https:\/\/doi.org\/10.1117\/12.640109.","journal-title":"NASA ADS"},{"key":"337_CR79","unstructured":"Tariq S, Lee S, Woo SS. A convolutional lstm based residual network for deepfake video detection. 2020. arXiv:2009.07480 [cs]."},{"key":"337_CR80","doi-asserted-by":"publisher","unstructured":"Marra F, Saltori C, Boato G, Verdoliva L. Incremental learning for the detection and classification of gan-generated images. In: 2019 IEEE international workshop on information forensics and security (WIFS). 2019. https:\/\/doi.org\/10.1109\/WIFS47025.2019.9035099. https:\/\/ieeexplore.ieee.org\/document\/9035099. Accessed 19 Aug 2022.","DOI":"10.1109\/WIFS47025.2019.9035099"},{"key":"337_CR81","doi-asserted-by":"publisher","first-page":"2153","DOI":"10.1016\/j.procs.2023.01.191","volume":"218","author":"M Kumar","year":"2023","unstructured":"Kumar M, Sharma HK. A gan-based model of deepfake detection in social media. Proc Comput Sci. 2023;218:2153\u201362.","journal-title":"Proc Comput Sci"},{"key":"337_CR82","doi-asserted-by":"crossref","unstructured":"Ko J, Cho K, Choi D, Ryoo K, Kim S. 3d gan inversion with pose optimization. In: Proceedings of the IEEE\/CVF Winter conference on applications of computer vision. 2023. p. 2967\u201376.","DOI":"10.1109\/WACV56688.2023.00298"},{"key":"337_CR83","unstructured":"Xu Y, Shu Z, Smith C, Huang JB, Oh SW. In-n-out: face video inversion and editing with volumetric decomposition. 2023."},{"key":"337_CR84","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1016\/j.neucom.2021.07.085","volume":"462","author":"X Nie","year":"2021","unstructured":"Nie X, Jia J, Ding H, Wong EK. Gigan: gate in gan, could gate mechanism filter the features in image-to-image translation? Neurocomputing. 2021;462:376\u201388.","journal-title":"Neurocomputing"},{"issue":"18","key":"337_CR85","doi-asserted-by":"publisher","first-page":"20704","DOI":"10.1109\/JSEN.2021.3100151","volume":"21","author":"Y Khaldi","year":"2021","unstructured":"Khaldi Y, Benzaoui A, Ouahabi A, Jacques S, Taleb-Ahmed A. Ear recognition based on deep unsupervised active learning. IEEE Sens J. 2021;21(18):20704\u201313.","journal-title":"IEEE Sens J"},{"key":"337_CR86","doi-asserted-by":"publisher","first-page":"28230","DOI":"10.1109\/ACCESS.2019.2901930","volume":"7","author":"W Fang","year":"2019","unstructured":"Fang W, Ding Y, Zhang F, Sheng J. Gesture recognition based on cnn and dcgan for calculation and text output. IEEE Access. 2019;7:28230\u20137.","journal-title":"IEEE Access"},{"key":"337_CR87","doi-asserted-by":"crossref","unstructured":"Zhu Y, Zhang Y, Yang H, Wang F. Gancoder: an automatic natural language-to-programming language translation approach based on gan. In: Natural language processing and chinese computing: 8th CCF international conference, NLPCC 2019. Berlin: Springer; 2019. p. 529\u201339.","DOI":"10.1007\/978-3-030-32236-6_48"},{"key":"337_CR88","unstructured":"Pasini M. Voice translation and audio style transfer with GANs. Towards Data Science. 2019."},{"key":"337_CR89","doi-asserted-by":"crossref","unstructured":"Battineni G, Chintalapudi N, Amenta F. Ai chatbot design during an epidemic like the novel coronavirus. In: Healthcare. 2020. p. 8.","DOI":"10.3390\/healthcare8020154"},{"key":"337_CR90","doi-asserted-by":"publisher","unstructured":"Yu N, Davis L, Fritz M. Attributing fake images to gans: learning and analyzing gan fingerprints. In: IEEE international conference on computer vision (ICCV). 2019. https:\/\/doi.org\/10.1109\/ICCV.2019.00765. Accessed 16 Aug 2020.","DOI":"10.1109\/ICCV.2019.00765"},{"key":"337_CR91","unstructured":"Bellemare MG, Danihelka I, Dabney W, Mohamed S, Lakshminarayanan B, Hoyer S, Munos R. The Cramer distance as a solution to biased Wasserstein gradients. 2017. arXiv:1705.10743."},{"issue":"8","key":"337_CR92","doi-asserted-by":"publisher","first-page":"128","DOI":"10.3390\/jimaging7080128","volume":"7","author":"O Giudice","year":"2021","unstructured":"Giudice O, Guarnera L, Battiato S. Fighting deepfakes by detecting gan dct anomalies. J Imaging. 2021;7(8):128. https:\/\/doi.org\/10.3390\/jimaging7080128.","journal-title":"J Imaging"},{"key":"337_CR93","doi-asserted-by":"publisher","unstructured":"Hulzebosch N, Ibrahimi S, Worring M. Detecting cnn-generated facial images in real-world scenarios. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops (CVPRW). 2020. https:\/\/doi.org\/10.1109\/CVPRW50498.2020.00329. https:\/\/ieeexplore.ieee.org\/document\/9150921. Accessed 19 Aug 2022.","DOI":"10.1109\/CVPRW50498.2020.00329"},{"key":"337_CR94","doi-asserted-by":"publisher","unstructured":"Fernandes S, Raj S, Ewetz R, Pannu JS, Kumar\u00a0Jha S, Ortiz E, Vintila I, Salter M. Detecting deepfake videos using attribution-based confidence metric. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition workshops (CVPRW). 2020. https:\/\/doi.org\/10.1109\/cvprw50498.2020.00162.","DOI":"10.1109\/cvprw50498.2020.00162"},{"key":"337_CR95","doi-asserted-by":"publisher","DOI":"10.1049\/iet-bmt.2019.0196","author":"C Rathgeb","year":"2020","unstructured":"Rathgeb C, Botaljov A, Stockhardt F, Isadskiy S, Debiasi L, Uhl A, Busch C. Prnu-based detection of facial retouching. IET Biometr. 2020. https:\/\/doi.org\/10.1049\/iet-bmt.2019.0196.","journal-title":"IET Biometr"},{"issue":"1","key":"337_CR96","first-page":"85","volume":"14","author":"W Alkishri","year":"2024","unstructured":"Alkishri W, Widyarto S, Yousif JH. Evaluating the effectiveness of a gan fingerprint removal approach in fooling deepfake face detection. J Internet Serv Info Secur (JISIS). 2024;14(1):85\u2013103.","journal-title":"J Internet Serv Info Secur (JISIS)"},{"key":"337_CR97","unstructured":"Perov I, Gao D, Chervoniy N, Liu K, Marangonda S, Um C, Dpfks M, Facenheim CS, RP L, Jiang J, Zhang S, Wu P, Zhou B, Zhang W. Deepfacelab: a simple, flexible and extensible face swapping framework. 2020. arXiv:2005.05535 [cs, eess]."},{"issue":"4","key":"337_CR98","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3197517.3201283","volume":"37","author":"H Kim","year":"2018","unstructured":"Kim H, Theobalt C, Carrido P, Tewari A, Xu W, Thies J, Niessner M, Prez P, Richardt C, Zollhfer M. Deep video portraits. ACM Trans Graph. 2018;37(4):1\u201314. https:\/\/doi.org\/10.1145\/3197517.3201283.","journal-title":"ACM Trans Graph"},{"issue":"4","key":"337_CR99","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1111\/cgf.14062","volume":"39","author":"J Naruniec","year":"2020","unstructured":"Naruniec J, Helminger L, Schroers C, Weber RM. High-resolution neural face swapping for visual effects. Comput Graph Forum. 2020;39(4):173\u201384. https:\/\/doi.org\/10.1111\/cgf.14062.","journal-title":"Comput Graph Forum"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00337-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-025-00337-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00337-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T10:43:10Z","timestamp":1750156990000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-025-00337-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,17]]},"references-count":99,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["337"],"URL":"https:\/\/doi.org\/10.1007\/s44163-025-00337-2","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,17]]},"assertion":[{"value":"17 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 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":"Not applicable. The study conducted in this paper was completed on real datasets that are publicly available on the GitHub repository. We have some of these datasets in Table .","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"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 interests"}}],"article-number":"109"}}