{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T15:02:24Z","timestamp":1770994944905,"version":"3.50.1"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T00:00:00Z","timestamp":1686268800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T00:00:00Z","timestamp":1686268800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s11042-023-15946-1","type":"journal-article","created":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T04:01:47Z","timestamp":1686283307000},"page":"7609-7630","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Steganography-based facial re-enactment using generative adversarial networks"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3460-6989","authenticated-orcid":false,"given":"Vijay","family":"Kumar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sahil","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,9]]},"reference":[{"key":"15946_CR1","doi-asserted-by":"publisher","unstructured":"Agustsson E, Timofte R (2017) NTIRE 2017 challenge on single image super-resolution: dataset and study. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). pp 126\u2013135. https:\/\/doi.org\/10.1109\/CVPRW.2017.151","DOI":"10.1109\/CVPRW.2017.151"},{"key":"15946_CR2","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1109\/TIFS.2018.2871749","volume":"14","author":"M Boroumand","year":"2018","unstructured":"Boroumand M, Chen M, Fridrich J (2018) Deep residual network for steganalysis of digital images. IEEE Trans Inf Forensics Secur 14:1181\u20131193","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"15946_CR3","unstructured":"Bounareli S, Argyriou V, Tzimiropoulos G (2022) Finding directions in GAN\u2019s latent space for neural face reenactment. arXiv preprint arXiv:2202.00046. pp 1\u201330"},{"key":"15946_CR4","doi-asserted-by":"publisher","unstructured":"Cao Q, Shen L, Xie W, Zisserman A (2018) VGGFace2: a dataset for recognising faces across pose and age. In: Proceeding 13th IEEE international conference on automatic face & gesture recognition. pp 67\u201374. https:\/\/doi.org\/10.1109\/FG.2018.00020","DOI":"10.1109\/FG.2018.00020"},{"key":"15946_CR5","unstructured":"Liu Z, Luo P, Wang X, Tang X (2018) Large-scale celebfaces attributes (celeba) dataset. Retrieved August, 15(2018). p 11"},{"key":"15946_CR6","unstructured":"Singla S, Singla S, Feizi S (2021) Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100. arXiv preprint arXiv:2108.04062"},{"key":"15946_CR7","doi-asserted-by":"publisher","unstructured":"Ciftci UA, Demir I, Yin L (2020) Fakecatcher: detection of synthetic portrait videos using biological signals. In: IEEE transactions on pattern analysis and machine intelligence. p 1. https:\/\/doi.org\/10.1109\/TPAMI.2020.3009287","DOI":"10.1109\/TPAMI.2020.3009287"},{"key":"15946_CR8","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) ImageNet: a large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition. pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"15946_CR9","doi-asserted-by":"crossref","unstructured":"Duan J, Duan J, Wang Y, Wan X (2022) Image steganography based on least bias generative adversarial network. In: International conference on cloud computing, performance computing, and deep learning. SPIE, vol 12287, pp 345\u2013350","DOI":"10.1117\/12.2640734"},{"key":"15946_CR10","unstructured":"Face Reenactment | Papers With Code. https:\/\/paperswithcode.com\/task\/face-reenactment. Accessed 1 Apr 2023"},{"key":"15946_CR11","unstructured":"Faces \u2013 Faces. https:\/\/www.visgraf.impa.br\/t-faces\/index.html. Accessed 1 Apr 2023"},{"key":"15946_CR12","unstructured":"Kowalski M, MarekKowalski\/FaceSwap: 3D face swapping implemented in python. GitHub. Available: https:\/\/github.com\/MarekKowalski\/FaceSwap. Accessed 1 Apr 2023"},{"key":"15946_CR13","unstructured":"Hao H, Baireddy S, Reibman AR, Delp EJ (2020) FaR-GAN for one-shot face reenactment. arXiv preprint arXiv:2005.06402"},{"key":"15946_CR14","doi-asserted-by":"publisher","first-page":"5464","DOI":"10.1109\/TIP.2019.2916751","volume":"28","author":"Z He","year":"2019","unstructured":"He Z, Zuo W, Kan M et al (2019) AttGAN: facial attribute editing by only changing what you want. IEEE Trans Image Process 28:5464\u20135478. https:\/\/doi.org\/10.1109\/TIP.2019.2916751","journal-title":"IEEE Trans Image Process"},{"key":"15946_CR15","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science (80-) 313:504\u2013507. https:\/\/doi.org\/10.1126\/science.1127647","journal-title":"Science (80-)"},{"issue":"2","key":"15946_CR16","first-page":"2155","volume":"3","author":"S Hiwe","year":"2014","unstructured":"Hiwe S, Nipanikar SI (2014) An analysis of image steganography methods. International Journal of Engineering Research & Technology 3(2):2155\u20132159","journal-title":"International Journal of Engineering Research & Technology"},{"key":"15946_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/J.IMAVIS.2022.104611","volume":"130","author":"C Hu","year":"2023","unstructured":"Hu C, Xie X, Wu L (2023) Face reenactment via generative landmark guidance. Image Vis Comput 130:104611. https:\/\/doi.org\/10.1016\/J.IMAVIS.2022.104611","journal-title":"Image Vis Comput"},{"key":"15946_CR18","doi-asserted-by":"publisher","unstructured":"Kae A, Sohn K, Lee H, Learned-Miller E (2013) Augmenting crfs with boltzmann machine shape priors for image labeling. In: Proceeding IEEE computer society conference on computer vision and pattern recognition. pp 2019\u20132026. https:\/\/doi.org\/10.1109\/CVPR.2013.263","DOI":"10.1109\/CVPR.2013.263"},{"key":"15946_CR19","doi-asserted-by":"publisher","first-page":"2001","DOI":"10.3390\/math10122001","volume":"10","author":"S Kamal","year":"2022","unstructured":"Kamal S, Sharma S, Kumar V et al (2022) trading stocks based on financial news using attention mechanism. Mathematics 10:2001","journal-title":"Mathematics"},{"key":"15946_CR20","unstructured":"Kerry CF (2018) Why protecting privacy is a losing game today\u2014and how to change the game. New York Times"},{"key":"15946_CR21","unstructured":"Korshunov P, Marcel S (2018) DeepFakes: a new threat to face recognition? assessment and detection. arXiv preprint arXiv:1812.08685"},{"key":"15946_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJDCF.318666","volume":"15","author":"V Kumar","year":"2023","unstructured":"Kumar V, Sharma S, Kumar C, Sahu AK (2023) Latest trends in deep learning techniques for image steganography. Int J Digit Crime Forensics 15:1\u201314","journal-title":"Int J Digit Crime Forensics"},{"key":"15946_CR23","unstructured":"Li L, Bao J, Yang H et al (2019) FaceShifter: towards high fidelity and occlusion aware face swapping. arXiv preprint arXiv:1912.13457"},{"key":"15946_CR24","doi-asserted-by":"crossref","unstructured":"Li Y, Yang X, Sun P, Qi, H, Lyu, S (2020) Celeb-DF: a large-scale challenging dataset for deepfake forensics. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 3207\u20133216","DOI":"10.1109\/CVPR42600.2020.00327"},{"issue":"LNCS","key":"15946_CR25","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48\/COVER","volume":"8693","author":"TY Lin","year":"2014","unstructured":"Lin TY, Maire M, Belongie S et al (2014) Microsoft COCO: common objects in context. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 8693(LNCS):740\u2013755. https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48\/COVER","journal-title":"Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics)"},{"key":"15946_CR26","doi-asserted-by":"publisher","unstructured":"Maze B, Adams J, Duncan JA, Kalka N, Miller T, Otto C, Jain AK, Niggel WT, Anderson J, Cheney J, Grother P (2018) IARPA janus benchmark-C: face dataset and protocol. In: Proceedings 2018 international conference on biometrics (ICB 2018). pp 158\u2013165. https:\/\/doi.org\/10.1109\/ICB2018.2018.00033","DOI":"10.1109\/ICB2018.2018.00033"},{"key":"15946_CR27","doi-asserted-by":"publisher","unstructured":"Nirkin Y, Masi I, Tu\u01cen AT, Hassner T, Medioni G (2018) On face segmentation, face swapping, and face perception. In: Proceedings 13th IEEE international conference on automatic face & gesture recognition (FG 2018). pp 98\u2013105. https:\/\/doi.org\/10.1109\/FG.2018.00024","DOI":"10.1109\/FG.2018.00024"},{"key":"15946_CR28","doi-asserted-by":"publisher","unstructured":"Nirkin Y, Keller Y, Hassner T (2019) FSGAN: subject agnostic face swapping and reenactment. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp 7183\u20137192. https:\/\/doi.org\/10.1109\/ICCV.2019.00728","DOI":"10.1109\/ICCV.2019.00728"},{"key":"15946_CR29","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1109\/TPAMI.2022.3155571","volume":"45","author":"Y Nirkin","year":"2023","unstructured":"Nirkin Y, Keller Y, Hassner T (2023) FSGANv2: improved subject agnostic face swapping and reenactment. IEEE Trans Pattern Anal Mach Intell 45:560\u2013575. https:\/\/doi.org\/10.1109\/TPAMI.2022.3155571","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"15946_CR30","unstructured":"NVlabs\/ffhq-dataset: Flickr-Faces-HQ Dataset (FFHQ). https:\/\/github.com\/NVlabs\/ffhq-dataset. Accessed 1 Apr 2023"},{"key":"15946_CR31","unstructured":"Patil K, Kale S, Dhokey J, Gulhane A (2023) Deepfake detection using biological features: a survey. arXiv preprint arXiv:2301.05819"},{"key":"15946_CR32","doi-asserted-by":"crossref","unstructured":"Ramaneti K, Kakani P, Krishna C, Rajkumar S (2021) Image steganography using GANs. In: Computer and information science 2021\u2014summer. Springer, Springer International Publishing, Cham, pp 169\u2013182","DOI":"10.1007\/978-3-030-79474-3_12"},{"key":"15946_CR33","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1016\/j.inffus.2022.12.007","volume":"92","author":"Y Rao","year":"2023","unstructured":"Rao Y, Wu D, Han M, Wang T, Yang Y, Lei T, Zhou C, Bai H, Xing L (2023) AT-GAN: a generative adversarial network with attention and transition for infrared and visible image fusion. Inf Fusion 92:336\u2013349","journal-title":"Inf Fusion"},{"key":"15946_CR34","doi-asserted-by":"crossref","unstructured":"Rosberg F, Aksoy EE, Alonso-Fernandez F, Englund C (2022) FaceDancer: pose-and occlusion-aware high fidelity face swapping. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision. pp 3454\u20133463","DOI":"10.1109\/WACV56688.2023.00345"},{"key":"15946_CR35","doi-asserted-by":"publisher","unstructured":"Rossler A, Cozzolino D, Verdoliva L, Riess C, Thies J, Nie\u00dfner M (2019) FaceForensics++: learning to detect manipulated facial images. In: Proceeding IEEE international conference on computer vision. pp 1\u201311. https:\/\/doi.org\/10.1109\/ICCV.2019.00009","DOI":"10.1109\/ICCV.2019.00009"},{"key":"15946_CR36","doi-asserted-by":"crossref","unstructured":"Sharma S, Kumar V (2019) Transfer learning in 2.5 D face image for occlusion presence and gender classification. In: Handbook of research on deep learning innovations and trends. IGI Global, pp 97\u2013113","DOI":"10.4018\/978-1-5225-7862-8.ch006"},{"key":"15946_CR37","doi-asserted-by":"publisher","first-page":"26517","DOI":"10.1007\/s11042-020-09331-5","volume":"79","author":"S Sharma","year":"2020","unstructured":"Sharma S, Kumar V (2020) Voxel-based 3D occlusion-invariant face recognition using game theory and simulated annealing. Multimed Tools Appl 79:26517\u201326547","journal-title":"Multimed Tools Appl"},{"key":"15946_CR38","doi-asserted-by":"publisher","first-page":"17303","DOI":"10.1007\/s11042-020-08688-x","volume":"79","author":"S Sharma","year":"2020","unstructured":"Sharma S, Kumar V (2020) Voxel-based 3D face reconstruction and its application to face recognition using sequential deep learning. Multimed Tools Appl 79:17303\u201317330","journal-title":"Multimed Tools Appl"},{"key":"15946_CR39","first-page":"1","volume":"18","author":"W Shi","year":"2022","unstructured":"Shi W, Liu S (2022) Hiding message using a cycle generative adversarial network. ACM Trans Multimed Comput Commun Appl 18:1\u201315","journal-title":"ACM Trans Multimed Comput Commun Appl"},{"key":"15946_CR40","doi-asserted-by":"crossref","unstructured":"Shu Z, Yumer E, Hadap S, Sunkavalli, K, Shechtman E, Samaras D (2017) Neural face editing with intrinsic image disentangling. pp 5541\u2013555041","DOI":"10.1109\/CVPR.2017.578"},{"key":"15946_CR41","doi-asserted-by":"publisher","first-page":"40511","DOI":"10.1007\/s11042-022-13172-9","volume":"81","author":"B Singh","year":"2022","unstructured":"Singh B, Sharma PK, Huddedar SA, Sur A, Mitra P (2022) StegGAN: hiding image within image using conditional generative adversarial networks. Multimed Tools Appl 81:40511\u201340533","journal-title":"Multimed Tools Appl"},{"key":"15946_CR42","unstructured":"Street View House Numbers (SVHN). Available at: https:\/\/www.kaggle.com\/datasets\/stanfordu\/street-view-house-numbers"},{"key":"15946_CR43","doi-asserted-by":"publisher","unstructured":"Svanera M, Muhammad UR, Leonardi R, Benini S (2016) Figaro, hair detection and segmentation in the wild. In: Proceedings International Conference on Image Processing (ICIP 2014-August). pp 933\u2013937. https:\/\/doi.org\/10.1109\/ICIP.2016.7532494","DOI":"10.1109\/ICIP.2016.7532494"},{"key":"15946_CR44","doi-asserted-by":"crossref","unstructured":"Thies J, Zollhofer M, Stamminger M et al (2016) Face2Face: real-time face capture and reenactment of rgb videos. pp 2387\u20132395","DOI":"10.1109\/CVPR.2016.262"},{"key":"15946_CR45","unstructured":"TNO Image Fusion Dataset. Available at: https:\/\/figshare.com\/articles\/dataset\/TNO_Image_Fusion_Dataset\/1008029"},{"key":"15946_CR46","doi-asserted-by":"publisher","unstructured":"Tripathy S, Kannala J, Rahtu E (2020) FACEGAN: facial attribute controllable reenactment GAN. In: Proceedings 2021 IEEE winter conference on applications of computer vision (WACV 2021). pp 1328\u20131337. https:\/\/doi.org\/10.48550\/arxiv.2011.04439","DOI":"10.48550\/arxiv.2011.04439"},{"key":"15946_CR47","doi-asserted-by":"publisher","unstructured":"Tripathy S, Kannala J, Rahtu E (2020) ICface: interpretable and controllable face reenactment using GANs. In: Proc - 2020 IEEE winter conference on applications of computer vision. WACV, pp 3374\u20133383. https:\/\/doi.org\/10.1109\/WACV45572.2020.9093474","DOI":"10.1109\/WACV45572.2020.9093474"},{"key":"15946_CR48","unstructured":"True color kodak images. Available at: http:\/\/r0k.us\/graphics\/kodak\/"},{"key":"15946_CR49","doi-asserted-by":"publisher","first-page":"749","DOI":"10.3390\/e24060749","volume":"24","author":"D Wang","year":"2022","unstructured":"Wang D, Li M, Zhang Y (2022) Adversarial data hiding in digital images. Entropy 24:749","journal-title":"Entropy"},{"key":"15946_CR50","unstructured":"Weber AG (1997) The USC-SIPI image database version 5 USC-SIPI Report 315. University of South California"},{"key":"15946_CR51","doi-asserted-by":"crossref","unstructured":"Wei P, Li S, Zhang X, Luo G, Qian Z, Zhou Q (2022) Generative steganography network. Association for computing machinery international conference on multimedia. pp 1621\u20131629","DOI":"10.1145\/3503161.3548217"},{"key":"15946_CR52","doi-asserted-by":"publisher","first-page":"54","DOI":"10.3390\/FI10060054","volume":"10","author":"P Wu","year":"2018","unstructured":"Wu P, Yang Y, Li X (2018) StegNet: mega image steganography capacity with deep Convolutional Network. Future Internet 10:54. https:\/\/doi.org\/10.3390\/FI10060054","journal-title":"Future Internet"},{"key":"15946_CR53","doi-asserted-by":"publisher","first-page":"8658","DOI":"10.1109\/TIP.2021.3112059","volume":"30","author":"X Wu","year":"2021","unstructured":"Wu X, Zhang Q, Wu Y, Wang H, Li S, Sun L, Li X (2021) F3A-GAN: facial flow for face animation with generative adversarial networks. IEEE Trans Image Process 30:8658\u20138670. https:\/\/doi.org\/10.1109\/TIP.2021.3112059","journal-title":"IEEE Trans Image Process"},{"key":"15946_CR54","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1016\/j.ins.2023.02.013","volume":"629","author":"Y Yang","year":"2023","unstructured":"Yang Y, Huang Y, Shi M, Chen K, Zhang W (2023) Invertible mask network for face privacy preservation. Inf Sci 629:566\u2013579","journal-title":"Inf Sci"},{"key":"15946_CR55","doi-asserted-by":"publisher","unstructured":"Yu F, Seff A, Zhang Y, Funkhouser T, Xiao J (2015) LSUN: construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365. https:\/\/doi.org\/10.48550\/arxiv.1506.03365","DOI":"10.48550\/arxiv.1506.03365"},{"key":"15946_CR56","unstructured":"Zhang KA, Cuesta-Infante A, Xu L, Veeramachaneni K (2019) SteganoGAN: high capacity image steganography with GANs. arXiv preprint arXiv:1901.03892"},{"key":"15946_CR57","doi-asserted-by":"publisher","unstructured":"Zhang T, Deng L, Zhang L, Dang X (2020) Deep learning in face synthesis: a survey on deepfakes. In: 2020 IEEE 3rd International Conference on Computer and Communication Engineering Technology (CCET). pp 67\u201370. https:\/\/doi.org\/10.1109\/CCET50901.2020.9213159","DOI":"10.1109\/CCET50901.2020.9213159"},{"key":"15946_CR58","doi-asserted-by":"crossref","unstructured":"Zhao Y, Liu B, Ding M, Liu B, Zhu T, Yu X (2023) Proactive deepfake defence via identity watermarking. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision. pp 4602\u20134611","DOI":"10.1109\/WACV56688.2023.00458"},{"key":"15946_CR59","doi-asserted-by":"crossref","unstructured":"Zhu X, Lei Z, Liu X, Shi H, Li SZ (2016) Face alignment across large poses: a 3D solution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp 146\u2013155","DOI":"10.1109\/CVPR.2016.23"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15946-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-15946-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15946-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T07:15:44Z","timestamp":1704698144000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-15946-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,9]]},"references-count":59,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["15946"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-15946-1","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,9]]},"assertion":[{"value":"24 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 April 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 May 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2023","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 of this manuscript declare no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}