{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T18:47:30Z","timestamp":1781808450830,"version":"3.54.5"},"publisher-location":"Cham","reference-count":105,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030876630","type":"print"},{"value":"9783030876647","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T00:00:00Z","timestamp":1643587200000},"content-version":"vor","delay-in-days":30,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Digital manipulation\u00a0has become a thriving topic in the last few years, especially after the popularity of the term DeepFakes. This chapter introduces the prominent digital manipulations with special emphasis on the facial content due to their large number of possible applications. Specifically, we cover the principles of six types of digital face manipulations: <jats:italic>(i)<\/jats:italic> entire face synthesis, <jats:italic>(ii)<\/jats:italic> identity swap, <jats:italic>(iii)<\/jats:italic> face morphing, <jats:italic>(iv)<\/jats:italic> attribute manipulation, <jats:italic>(v)<\/jats:italic> expression swap (a.k.a. face reenactment\u00a0or talking faces), and <jats:italic>(vi)<\/jats:italic> audio- and text-to-video. These six main types of face manipulation\u00a0are well established by the research community, having received the most attention in the last few years. In addition, we highlight in this chapter publicly available databases and code for the generation of digital fake content.<\/jats:p>","DOI":"10.1007\/978-3-030-87664-7_1","type":"book-chapter","created":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T09:03:06Z","timestamp":1643619786000},"page":"3-26","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["An Introduction to\u00a0Digital Face Manipulation"],"prefix":"10.1007","author":[{"given":"Ruben","family":"Tolosana","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruben","family":"Vera-Rodriguez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julian","family":"Fierrez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aythami","family":"Morales","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Javier","family":"Ortega-Garcia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,31]]},"reference":[{"key":"1_CR1","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.inffus.2020.06.014","volume":"64","author":"R Tolosana","year":"2020","unstructured":"Tolosana R, Vera-Rodriguez R, Fierrez J, Morales A, Ortega-Garcia J (2020) DeepFakes and beyond: a survey of face manipulation and fake detection. Inform Fusion 64:131\u2013148","journal-title":"Inform Fusion"},{"issue":"2","key":"1_CR2","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1109\/MSP.2008.931079","volume":"26","author":"H Farid","year":"2009","unstructured":"Farid H (2009) Image forgery detection. IEEE Signal Process Mag 26(2):16\u201325","journal-title":"IEEE Signal Process Mag"},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Milani S, Fontani M, Bestagini P, Barni M, Piva A, Tagliasacchi M, Tubaro S (2012) An overview on video forensics. APSIPA Trans Signal Inform Process 1","DOI":"10.1017\/ATSIP.2012.2"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Piva A (2013) An overview on image forensics. ISRN Signal Process","DOI":"10.1155\/2013\/496701"},{"issue":"2","key":"1_CR5","first-page":"353","volume":"31","author":"C Bregler","year":"1997","unstructured":"Bregler C, Covell M, Slaney M (1997) Video rewrite: driving visual speech with audio. Comput Graph 31(2):353\u2013361","journal-title":"Comput Graph"},{"key":"1_CR6","unstructured":"Information Technology-Biometric Presentation Attack Detection-Part 3: Testing and Reporting. Technical report, ISO\/IEC JTC1 SC37 Biometrics (2017)"},{"key":"1_CR7","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Proceedings of advances in neural information processing systems"},{"key":"1_CR8","unstructured":"Kingma DP, Welling M (2013) Auto-encoding variational bayes. In: Proceedings of international conference on learning representations"},{"key":"1_CR9","unstructured":"Cellan-Jones R (2019) Deepfake videos double in nine months. https:\/\/www.bbc.com\/news\/technology-49961089"},{"key":"1_CR10","unstructured":"Citron D (2019) How DeepFake undermine truth and threaten democracy. https:\/\/www.ted.com"},{"key":"1_CR11","unstructured":"Korshunov P, Marcel S (2018) Deepfakes: a new threat to face recognition? Assessment and detection. arXiv preprint arXiv:1812.08685"},{"key":"1_CR12","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1109\/JSTSP.2020.3002101","volume":"14","author":"L Verdoliva","year":"2020","unstructured":"Verdoliva L (2020) Media forensics and DeepFakes: an overview. IEEE J Sel Top Signal Process 14:910\u2013932","journal-title":"IEEE J Sel Top Signal Process"},{"key":"1_CR13","unstructured":"BBC Bitesize: Deepfakes: what are they and why would i make one? (2019). https:\/\/www.bbc.co.uk\/bitesize\/articles\/zfkwcqt"},{"issue":"2","key":"1_CR14","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.bushor.2019.11.006","volume":"63","author":"J Kietzmann","year":"2020","unstructured":"Kietzmann J, Lee LW, McCarthy IP, Kietzmann TC (2020) Deepfakes: trick or treat? Business Horizons 63(2):135\u2013146","journal-title":"Business Horizons"},{"key":"1_CR15","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 (2017) Digital image integrity-a survey of protection and verification techniques. Digital Signal Process 71:1\u201326","journal-title":"Digital Signal Process"},{"issue":"4","key":"1_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1978802.1978805","volume":"43","author":"A Rocha","year":"2011","unstructured":"Rocha A, Scheirer W, Boult T, Goldenstein S (2011) Vision of the unseen: current trends and challenges in digital image and video forensics. ACM Comput Surv 43(4):1\u201342","journal-title":"ACM Comput Surv"},{"issue":"3","key":"1_CR17","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1109\/TIFS.2010.2053202","volume":"5","author":"M Stamm","year":"2010","unstructured":"Stamm M, Liu K (2010) Forensic detection of image manipulation using statistical intrinsic fingerprints. IEEE Trans Inform Forensics Secur 5(3):492\u2013506","journal-title":"IEEE Trans Inform Forensics Secur"},{"issue":"1","key":"1_CR18","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1109\/TIFS.2007.916010","volume":"3","author":"A Swaminathan","year":"2008","unstructured":"Swaminathan A, Wu M, Liu KJR (2008) Digital image forensics via intrinsic fingerprints. IEEE Trans Inform Forensics Secur 3(1):101\u2013117","journal-title":"IEEE Trans Inform Forensics Secur"},{"key":"1_CR19","doi-asserted-by":"crossref","unstructured":"Cozzolino D, R\u00f6ssler A, Thies J, Nie\u00dfner M, Verdoliva L (2020) ID-Reveal: identity-aware DeepFake video detection. arXiv preprint arXiv:2012.02512","DOI":"10.1109\/ICCV48922.2021.01483"},{"key":"1_CR20","doi-asserted-by":"crossref","unstructured":"R\u00f6ssler A, Cozzolino D, Verdoliva L, Riess C, Thies J, Nie\u00dfner M (2019) FaceForensics++: learning to detect manipulated facial images. In: Proceedinsg of IEEE\/CVF international conference on computer vision","DOI":"10.1109\/ICCV.2019.00009"},{"key":"1_CR21","doi-asserted-by":"publisher","first-page":"1530","DOI":"10.1109\/ACCESS.2014.2381273","volume":"2","author":"J Galbally","year":"2014","unstructured":"Galbally J, Marcel S, Fierrez J (2014) Biometric anti-spoofing methods: a survey in face recognition. IEEE Access 2:1530\u20131552","journal-title":"IEEE Access"},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Hadid A, Evans N, Marcel S, Fierrez J (2015) Biometrics systems under spoofing attack: an evaluation methodology and lessons learned. IEEE Signal Process Mag","DOI":"10.1109\/MSP.2015.2437652"},{"key":"1_CR23","doi-asserted-by":"crossref","unstructured":"Marcel S, Nixon M, Fierrez J, Evans N (2019) Handbook of biometric anti-spoofing, 2nd edn","DOI":"10.1007\/978-3-319-92627-8"},{"key":"1_CR24","doi-asserted-by":"crossref","unstructured":"Dang H, Liu F, Stehouwer J, Liu X, Jain A (2020) On the detection of digital face manipulation. In: Proceedings of IEEE\/CVF conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR42600.2020.00582"},{"key":"1_CR25","doi-asserted-by":"crossref","unstructured":"Hernandez-Ortega J, Tolosana R, Fierrez J, Morales A (2021) DeepFakesON-Phys: DeepFakes detection based on heart rate estimation. In: Proceedings of 35th AAAI conference on artificial intelligence workshops","DOI":"10.1007\/978-3-030-87664-7_12"},{"issue":"5","key":"1_CR26","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 (2020) GANprintR: improved fakes and evaluation of the state of the art in face manipulation detection. IEEE J Sel Top Signal Process 14(5):1038\u20131048","journal-title":"IEEE J Sel Top Signal Process"},{"key":"1_CR27","doi-asserted-by":"crossref","unstructured":"Tolosana R, Romero-Tapiador S, Fierrez J, Vera-Rodriguez R (2021) DeepFakes evolution: analysis of facial regions and fake detection performance. In: Proceedings of international conference on pattern recognition workshops","DOI":"10.1007\/978-3-030-68821-9_38"},{"key":"1_CR28","unstructured":"Barni M, Battiato S, Boato G, Farid H, Memon N (2020) Multimedia forensics in the wild. In: International conference on pattern recognition. https:\/\/iplab.dmi.unict.it\/mmforwild\/"},{"key":"1_CR29","unstructured":"Biggio B, Korshunov P, Mensink T, Patrini G, Rao D, Sadhu A (2019) Synthetic realities: deep learning for detecting audio visual fakes. In: International conference on machine learning. https:\/\/sites.google.com\/view\/audiovisualfakes-icml2019\/"},{"key":"1_CR30","unstructured":"Gregory S, Canton C, Leal-Taix\u00e9 L, Bregler C, Farid H, Nie\u00dfner M, Escalera S, Delp E, McCloskey S, Guyon I, Basharat A, Thies J, Verdoliva L, Escalante HJ, Scharfenberg C, R\u00f6ssler A, Wan J, Cozzolino D, Guodong G (2020) Workshop on media forensics. In: Conference on computer vision and pattern recognition. https:\/\/sites.google.com\/view\/wmediaforensics2020\/home"},{"key":"1_CR31","unstructured":"Raja K, Damer N, Chen C, Dantcheva A, Czajka A, Han H, Ramachandra R (2020) Workshop on Deepfakes and presentation attacks in biometrics. In: Winter conference on applications of computer vision. https:\/\/sites.google.com\/view\/wacv2020-deeppab\/"},{"key":"1_CR32","doi-asserted-by":"crossref","unstructured":"Verdoliva L, Bestagini P (2019) Multimedia forensics. ACM Multimed. https:\/\/acmmm.org\/tutorials\/#tut3","DOI":"10.1145\/3343031.3350542"},{"issue":"1","key":"1_CR33","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/s11263-010-0403-1","volume":"92","author":"I Yerushalmy","year":"2011","unstructured":"Yerushalmy I, Hel-Or H (2011) Digital image forgery detection based on lens and sensor aberration. Int J Comput Vis 92(1):71\u201391","journal-title":"Int J Comput Vis"},{"issue":"4","key":"1_CR34","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1109\/TIFS.2009.2033749","volume":"4","author":"H Cao","year":"2009","unstructured":"Cao H, Kot AC (2009) Accurate detection of demosaicing regularity for digital image forensics. IEEE Trans Inform Forensics Secur 4(4):899\u2013910","journal-title":"IEEE Trans Inform Forensics Secur"},{"issue":"10","key":"1_CR35","doi-asserted-by":"publisher","first-page":"3948","DOI":"10.1109\/TSP.2005.855406","volume":"53","author":"AC Popescu","year":"2005","unstructured":"Popescu AC, Farid H (2005) Exposing digital forgeries in color filter array interpolated images. IEEE Trans Signal Process 53(10):3948\u20133959","journal-title":"IEEE Trans Signal Process"},{"issue":"2","key":"1_CR36","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1109\/TIFS.2011.2106121","volume":"6","author":"YL Chen","year":"2011","unstructured":"Chen YL, Hsu CT (2011) Detecting recompression of jpeg images via periodicity analysis of compression artifacts for tampering detection. IEEE Trans Inform Forensics Secur 6(2):396\u2013406","journal-title":"IEEE Trans Inform Forensics Secur"},{"issue":"11","key":"1_CR37","doi-asserted-by":"publisher","first-page":"2492","DOI":"10.1016\/j.patcog.2009.03.019","volume":"42","author":"Z Lin","year":"2009","unstructured":"Lin Z, He J, Tang X, Tang C (2009) Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis. Pattern Recogn 42(11):2492\u20132501","journal-title":"Pattern Recogn"},{"issue":"3","key":"1_CR38","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.1109\/TIFS.2011.2129512","volume":"6","author":"I Amerini","year":"2011","unstructured":"Amerini I, Ballan L, Caldelli R, Bimbo A, Serra G (2011) A sift-based forensic method for copy-move attack detection and transformation recovery. IEEE Trans Inform Forensics Secur 6(3):1099\u20131110","journal-title":"IEEE Trans Inform Forensics Secur"},{"key":"1_CR39","doi-asserted-by":"crossref","unstructured":"Cozzolino D, Poggi G, Verdoliva L (2015) Splicebuster: a new blind image splicing detector. In: Proceedings of IEEE international workshop on information forensics and security, pp 1\u20136","DOI":"10.1109\/WIFS.2015.7368565"},{"key":"1_CR40","doi-asserted-by":"crossref","unstructured":"Gironi A, Fontani M, Bianchi T, Piva A, Barni M (2014) A video forensic technique for detecting frame deletion and insertion. In: Proceedings of IEEE international conference on acoustics, speech and signal processing, pp 6226\u20136230","DOI":"10.1109\/ICASSP.2014.6854801"},{"key":"1_CR41","doi-asserted-by":"crossref","unstructured":"Wu Y, Jiang X, Sun T, Wang W (2014) Exposing video inter-frame forgery based on velocity field consistency. In: Proceedinsg of IEEE international conference on acoustics, speech and signal processing, pp 2674\u20132678","DOI":"10.1109\/ICASSP.2014.6854085"},{"issue":"2","key":"1_CR42","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1257\/jep.31.2.211","volume":"31","author":"H Allcott","year":"2017","unstructured":"Allcott H, Gentzkow M (2017) Social media and fake news in the 2016 election. J Econ Perspect 31(2):211\u2013236","journal-title":"J Econ Perspect"},{"issue":"6380","key":"1_CR43","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1126\/science.aao2998","volume":"359","author":"DM Lazer","year":"2018","unstructured":"Lazer DM, Baum MA, Benkler Y, Berinsky AJ, Greenhill KM, Menczer F, Metzger MJ, Nyhan B, Pennycook G, Rothschild D et al (2018) The science of fake news. Science 359(6380):1094\u20131096","journal-title":"Science"},{"key":"1_CR44","unstructured":"Karras T, Aila T, Laine S, Lehtinen J (2018) Progressive growing of GANs for improved quality, stability, and variation. In: Proceedings of international conference on learning representations"},{"key":"1_CR45","doi-asserted-by":"crossref","unstructured":"Liu Z, Luo P, Wang X, Tang X (2015) Deep learning face attributes in the wild. In: Proceedings of IEEE\/CVF international conference on computer vision","DOI":"10.1109\/ICCV.2015.425"},{"key":"1_CR46","doi-asserted-by":"crossref","unstructured":"Karras T, Laine S, Aila T (2019) A style-based generator architecture for generative adversarial networks. In: Proceedings of IEEE\/CVF conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2019.00453"},{"key":"1_CR47","doi-asserted-by":"crossref","unstructured":"Huang X, Belongie S (2017) Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of IEEE\/CVF international conference on computer vision","DOI":"10.1109\/ICCV.2017.167"},{"key":"1_CR48","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. In: Proceedings of IEEE\/CVF conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"1_CR49","unstructured":"Karras T, Aittala M, Hellsten J, Laine S, Lehtinen J, Aila T (2020) Training Generative adversarial networks with limited data. arXiv preprint arXiv:2006.06676"},{"key":"1_CR50","unstructured":"Albright M, McCloskey S (2019) Source generator attribution via inversion. In: Proceedings of conference on computer vision and pattern recognition workshops"},{"key":"1_CR51","doi-asserted-by":"crossref","unstructured":"Marra F, Gragnaniello D, Verdoliva L, Poggi G (2019) Do GANs leave artificial fingerprints? In: Proceedings of IEEE conference on multimedia information processing and retrieval, pp 506\u2013511","DOI":"10.1109\/MIPR.2019.00103"},{"key":"1_CR52","unstructured":"100,000 faces generated by AI (2018). https:\/\/generated.photos\/"},{"key":"1_CR53","unstructured":"Yi D, Lei Z, Liao S, Li S (2014) Learning face representation from scratch. arXiv preprint arXiv:1411.7923"},{"key":"1_CR54","doi-asserted-by":"crossref","unstructured":"Cao Q, Shen L, Xie W, Parkhi O, Zisserman A (2018) VGGFace2: a dataset for recognising faces across pose and age. In: Proceedings of international conference on automatic face & gesture recognition","DOI":"10.1109\/FG.2018.00020"},{"key":"1_CR55","doi-asserted-by":"crossref","unstructured":"Nech A, Kemelmacher-Shlizerman I (2017) Level playing field for million scale face recognition. In: Proceedings of IEEE\/CVF conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2017.363"},{"key":"1_CR56","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 IEEE\/CVF conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR42600.2020.00327"},{"issue":"1","key":"1_CR57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3425780","volume":"54","author":"Y Mirsky","year":"2021","unstructured":"Mirsky Y, Lee W (2021) The creation and detection of Deepfakes: a survey. ACM Comput Surv 54(1):1\u201341","journal-title":"ACM Comput Surv"},{"key":"1_CR58","doi-asserted-by":"crossref","unstructured":"Li Y, Chang M, Lyu S (2018) In Ictu Oculi: exposing AI generated fake face videos by detecting eye blinking. In: Proceedings of international workshop on information forensics and security","DOI":"10.1109\/WIFS.2018.8630787"},{"key":"1_CR59","unstructured":"Dolhansky B, Howes R, Pflaum B, Baram N, Ferrer C (2019) The Deepfake detection challenge (DFDC) preview dataset. arXiv preprint arXiv:1910.08854"},{"key":"1_CR60","doi-asserted-by":"crossref","unstructured":"Jiang L, Wu W, Li R, Qian C, Loy CC (2020) DeeperForensics-1.0: a large-scale dataset for real-world face forgery detection. In: Proceedings of IEEE\/CVF conference on computer vision and pattern recognition (2020)","DOI":"10.1109\/CVPR42600.2020.00296"},{"key":"1_CR61","doi-asserted-by":"crossref","unstructured":"Zi B, Chang M, Chen J, Ma X, Jiang YG (2020) WildDeepfake: a challenging real-world dataset for deepfake detection. In: Proceedings of ACM international conference on multimedia","DOI":"10.1145\/3394171.3413769"},{"key":"1_CR62","doi-asserted-by":"crossref","unstructured":"Sanderson C, Lovell B (2009) Multi-region probabilistic histograms for robust and scalable identity inference. In: Proceedings of international conference on biometrics","DOI":"10.1007\/978-3-642-01793-3_21"},{"key":"1_CR63","doi-asserted-by":"crossref","unstructured":"Zhu J, Park T, Isola P, Efros A (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of international conference on computer vision (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"1_CR64","doi-asserted-by":"crossref","unstructured":"Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: Proceedings of IEEE\/CVF conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2015.7298682"},{"issue":"10","key":"1_CR65","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","volume":"23","author":"K Zhang","year":"2016","unstructured":"Zhang K, Zhang Z, Li Z, Qiao Y (2016) Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process Lett 23(10):1499\u20131503","journal-title":"IEEE Signal Process Lett"},{"key":"1_CR66","unstructured":"Google AI: Contributing data to Deepfake detection research (2019). https:\/\/ai.googleblog.com\/2019\/09\/contributing-data-to-deepfake-detection.html"},{"key":"1_CR67","unstructured":"Dolhansky B, Bitton J, Pflaum B, Lu J, Howes R, Wang M, Ferrer CC (2020) The DeepFake detection challenge dataset. arXiv preprint arXiv:2006.07397"},{"key":"1_CR68","unstructured":"R\u00f6ssler A, Cozzolino D, Verdoliva L, Riess C, Thies J, Nie\u00dfner M (2018) FaceForensics: a large-scale video dataset for forgery detection in human faces. arXiv preprint arXiv:1803.09179"},{"issue":"3","key":"1_CR69","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1145\/882262.882269","volume":"22","author":"P P\u00e9rez","year":"2003","unstructured":"P\u00e9rez P, Gangnet M, Blake A (2003) Poisson image editing. ACM Trans Graph 22(3):313\u2013318","journal-title":"Poisson image editing. ACM Trans Graph"},{"key":"1_CR70","doi-asserted-by":"publisher","first-page":"23012","DOI":"10.1109\/ACCESS.2019.2899367","volume":"7","author":"U Scherhag","year":"2019","unstructured":"Scherhag U, Rathgeb C, Merkle J, Breithaupt R, Busch C (2019) Face recognition systems under morphing attacks: a survey. IEEE Access 7:23012\u201323026","journal-title":"IEEE Access"},{"issue":"8\u20139","key":"1_CR71","first-page":"360","volume":"14","author":"G Wolberg","year":"1998","unstructured":"Wolberg G (1998) Image morphing: a survey. Vis Comput 14(8\u20139):360\u2013372","journal-title":"Image morphing: a survey. Vis Comput"},{"key":"1_CR72","doi-asserted-by":"crossref","unstructured":"Gomez-Barrero M, Rathgeb C, Scherhag U, Busch C (2017) Is your biometric system robust to morphing attacks? In: Proceedings of IEEE international workshop on biometrics and forensics","DOI":"10.1109\/IWBF.2017.7935079"},{"key":"1_CR73","unstructured":"Korshunov P, Marcel S (2019) Vulnerability of face recognition to deep morphing. arXiv preprint arXiv:1910.01933"},{"key":"1_CR74","doi-asserted-by":"crossref","unstructured":"Venkatesh S, Raghavendra R, Raja K, Busch C (2021) Face morphing attack generation & detection: a comprehensive survey. IEEE Trans Technol Soc","DOI":"10.1109\/TTS.2021.3066254"},{"key":"1_CR75","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1111\/cgf.12214","volume":"32","author":"Y Weng","year":"2013","unstructured":"Weng Y, Wang L, Li X, Chai M, Zhou K (2013) Hair interpolation for portrait morphing. Comput Graph Forum 32:79\u201384","journal-title":"Comput Graph Forum"},{"key":"1_CR76","doi-asserted-by":"crossref","unstructured":"Zhang H, Venkatesh S, Ramachandra R, Raja K, Damer N, Busch C (2021) MIPGAN-generating strong and high quality morphing attacks using identity prior driven GAN. arXiv preprint arXiv:2009.01729","DOI":"10.1109\/TBIOM.2021.3072349"},{"key":"1_CR77","volume-title":"Morphing attack detection-database","author":"K Raja","year":"2020","unstructured":"Raja K, Ferrara M, Franco A, Spreeuwers L, Batskos I, de Wit F, Gomez-Barrero M, Scherhag U, Fischer D, Venkatesh S, Singh JM, Li G, Bergeron L, Isadskiy S, Ramachandra R, Rathgeb C, Frings D, Seidel U, Knopjes F, Veldhuis R, Maltoni D, Busch C (2020) Morphing attack detection-database. Evaluation platform and benchmarking, IEEE Trans Inform Forensics Secur"},{"issue":"4","key":"1_CR78","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1049\/iet-bmt.2017.0147","volume":"7","author":"T Neubert","year":"2018","unstructured":"Neubert T, Makrushin A, Hildebrandt M, Kraetzer C, Dittmann J (2018) Extended StirTrace benchmarking of biometric and forensic qualities of morphed face images. IET Biometrics 7(4):325\u2013332","journal-title":"IET Biometrics"},{"issue":"8","key":"1_CR79","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 (2018) Facial soft biometrics for recognition in the wild: recent works, annotation and COTS evaluation. IEEE Trans Inform Forensics Secur 13(8):2001\u20132014","journal-title":"IEEE Trans Inform Forensics Secur"},{"key":"1_CR80","doi-asserted-by":"crossref","unstructured":"Choi Y, Choi M, Kim M, Ha J, Kim S, Choo J (2018) StarGAN: unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of IEEE\/CVF conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2018.00916"},{"key":"1_CR81","unstructured":"FaceApp (2017). https:\/\/apps.apple.com\/cn\/app\/id1465199127"},{"key":"1_CR82","doi-asserted-by":"crossref","unstructured":"He Z, Zuo W, Kan M, Shan S, Chen X (2019) AttGAN: facial attribute editing by only changing what you want. IEEE Trans Image Process","DOI":"10.1109\/TIP.2019.2916751"},{"key":"1_CR83","unstructured":"Lample G, Zeghidour N, Usunier N, Bordes A, Denoyer L, Ranzato M (2017) Fader networks: manipulating images by sliding attributes. In: Proceedings of advances in neural information processing systems"},{"key":"1_CR84","doi-asserted-by":"crossref","unstructured":"Liu M, Ding Y, Xia M, Liu X, Ding E, Zuo W, Wen S (2019) STGAN: a unified selective transfer network for arbitrary image attribute editing. In: Proceedings of IEEE\/CVF conference on computer vision and pattern recognition (2019)","DOI":"10.1109\/CVPR.2019.00379"},{"key":"1_CR85","unstructured":"Li M, Zuo W, Zhang D (2016) Deep identity-aware transfer of facial attributes. arXiv preprint arXiv:1610.05586"},{"key":"1_CR86","unstructured":"Perarnau G, Weijer JVD, Raducanu B, \u00c1lvarez J (2016) Invertible conditional GANs for image editing. In: Proceedings of advances in neural information processing systems workshops"},{"key":"1_CR87","doi-asserted-by":"crossref","unstructured":"Shen W, Liu R (2017) Learning residual images for face attribute manipulation. In: Proceedings of conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2017.135"},{"key":"1_CR88","doi-asserted-by":"crossref","unstructured":"Xiao T, Hong J, Ma J (2018) ELEGANT: exchanging latent encodings with GAN for transferring multiple face attributes. In: Proceedings of European conference on computer vision","DOI":"10.1007\/978-3-030-01249-6_11"},{"key":"1_CR89","unstructured":"Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784"},{"key":"1_CR90","doi-asserted-by":"crossref","unstructured":"Chu W, Tai Y, Wang C, Li J, Huang F, Ji R (2020) SSCGAN: facial attribute editing via style skip connections. In: Proceedings of European conference on computer vision","DOI":"10.1007\/978-3-030-58555-6_25"},{"key":"1_CR91","doi-asserted-by":"crossref","unstructured":"Wu PW, Lin YJ, Chang CH, Chang EY, Liao SW (2019) RelGAN: multi-domain image-to-image translation via relative attributes. In: Proceedings of IEEE\/CVF international conference on computer vision","DOI":"10.1109\/ICCV.2019.00601"},{"issue":"66","key":"1_CR92","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3306346.3323035","volume":"38","author":"J Thies","year":"2019","unstructured":"Thies J, Zollh\u00f6fer M, Nie\u00dfner M (2019) Deferred neural rendering: image synthesis using neural textures. ACM Trans Graph 38(66):1\u201312","journal-title":"ACM Trans Graph"},{"key":"1_CR93","doi-asserted-by":"crossref","unstructured":"Thies J, Zollhofer M, Stamminger M, Theobalt C, Nie\u00dfner M (2016) Face2face: real-time face capture and reenactment of RGB videos. In: Proceedings of IEEE\/CVF conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2016.262"},{"key":"1_CR94","doi-asserted-by":"crossref","unstructured":"Isola P, Zhu J, Zhou T, Efros A (2017) Image-to-image translation with conditional adversarial networks. In: Proceedings of conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2017.632"},{"issue":"6","key":"1_CR95","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1145\/3130800.3130818","volume":"36","author":"H Averbuch-Elor","year":"2017","unstructured":"Averbuch-Elor H, Cohen-Or D, Kopf J, Cohen MF (2017) Bringing portraits to life. ACM Trans Graph 36(6):196","journal-title":"ACM Trans Graph"},{"key":"1_CR96","doi-asserted-by":"crossref","unstructured":"Ha S, Kersner M, Kim B, Seo S, Kim D (2020) Marionette: few-shot face reenactment preserving identity of unseen targets. In: Proceedings of AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v34i07.6721"},{"key":"1_CR97","unstructured":"Siarohin A, Lathuili\u00e8re S, Tulyakov S, Ricci E, Sebe N (2019) First order motion model for image animation. In: Proceedings of advances in neural information processing systems"},{"key":"1_CR98","doi-asserted-by":"crossref","unstructured":"Zakharov E, Shysheya A, Burkov E, Lempitsky V (2019) Few-shot adversarial learning of realistic neural talking head models. In: Proceedings of IEEE\/CVF international conference on computer vision","DOI":"10.1109\/ICCV.2019.00955"},{"key":"1_CR99","doi-asserted-by":"crossref","unstructured":"Agarwal S, Farid H, Fried O, Agrawala M (2020) Detecting deep-fake videos from phoneme-viseme mismatches. In: Proceedings of workshop on media forensics, CVPRw","DOI":"10.1109\/CVPRW50498.2020.00338"},{"key":"1_CR100","doi-asserted-by":"crossref","unstructured":"Thies J, Elgharib M, Tewari A, Theobalt C, Nie\u00dfner M (2020) Neural voice puppetry: audio-driven facial reenactment. In: Proceedings of European conference on computer vision","DOI":"10.1007\/978-3-030-58517-4_42"},{"issue":"4","key":"1_CR101","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3072959.3073640","volume":"36","author":"S Suwajanakorn","year":"2017","unstructured":"Suwajanakorn S, Seitz S, Kemelmacher-Shlizerman I (2017) Synthesizing Obama: Learning Lip Sync From Audio. ACM Transactions on Graphics 36(4):1\u201313","journal-title":"ACM Transactions on Graphics"},{"key":"1_CR102","doi-asserted-by":"crossref","unstructured":"Song Y, Zhu J, Li D, Wang A, Qi H (2019) Talking face generation by conditional recurrent adversarial network. In: Proceedings of international joint conference on artificial intelligence","DOI":"10.24963\/ijcai.2019\/129"},{"key":"1_CR103","unstructured":"Song L, Wu W, Qian C, He R, Loy C (2020) Everybody\u2019s Talkin\u2019: let me talk as you want. arXiv preprint arXiv:2001.05201"},{"key":"1_CR104","doi-asserted-by":"crossref","unstructured":"Zhou H, Liu Y, Liu Z, Luo P, Wang X (2019) Talking face generation by adversarially disentangled audio-visual representation. In: Proceedings of AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v33i01.33019299"},{"key":"1_CR105","doi-asserted-by":"crossref","unstructured":"Fried O, Tewari A, Zollh\u00f6fer M, Finkelstein A, Shechtman E, Goldman DB, Genova K, Jin Z, Theobalt C, Agrawala M (2019) Text-based editing of talking-head video. ACM Trans Graph 38(4)","DOI":"10.1145\/3306346.3323028"}],"container-title":["Advances in Computer Vision and Pattern Recognition","Handbook of Digital Face Manipulation and Detection"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87664-7_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T07:04:26Z","timestamp":1707462266000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87664-7_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030876630","9783030876647"],"references-count":105,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87664-7_1","relation":{},"ISSN":["2191-6586","2191-6594"],"issn-type":[{"value":"2191-6586","type":"print"},{"value":"2191-6594","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"31 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}