{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T16:41:33Z","timestamp":1781282493770,"version":"3.54.1"},"reference-count":119,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T00:00:00Z","timestamp":1655337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European University of the Atlantic"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Deep learning is used to address a wide range of challenging issues including large data analysis, image processing, object detection, and autonomous control. In the same way, deep learning techniques are also used to develop software and techniques that pose a danger to privacy, democracy, and national security. Fake content in the form of images and videos using digital manipulation with artificial intelligence (AI) approaches has become widespread during the past few years. Deepfakes, in the form of audio, images, and videos, have become a major concern during the past few years. Complemented by artificial intelligence, deepfakes swap the face of one person with the other and generate hyper-realistic videos. Accompanying the speed of social media, deepfakes can immediately reach millions of people and can be very dangerous to make fake news, hoaxes, and fraud. Besides the well-known movie stars, politicians have been victims of deepfakes in the past, especially US presidents Barak Obama and Donald Trump, however, the public at large can be the target of deepfakes. To overcome the challenge of deepfake identification and mitigate its impact, large efforts have been carried out to devise novel methods to detect face manipulation. This study also discusses how to counter the threats from deepfake technology and alleviate its impact. The outcomes recommend that despite a serious threat to society, business, and political institutions, they can be combated through appropriate policies, regulation, individual actions, training, and education. In addition, the evolution of technology is desired for deepfake identification, content authentication, and deepfake prevention. Different studies have performed deepfake detection using machine learning and deep learning techniques such as support vector machine, random forest, multilayer perceptron, k-nearest neighbors, convolutional neural networks with and without long short-term memory, and other similar models. This study aims to highlight the recent research in deepfake images and video detection, such as deepfake creation, various detection algorithms on self-made datasets, and existing benchmark datasets.<\/jats:p>","DOI":"10.3390\/s22124556","type":"journal-article","created":{"date-parts":[[2022,6,19]],"date-time":"2022-06-19T21:19:26Z","timestamp":1655673566000},"page":"4556","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["A Review of Image Processing Techniques for Deepfakes"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6151-3713","authenticated-orcid":false,"given":"Hina Fatima","family":"Shahzad","sequence":"first","affiliation":[{"name":"Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8403-1047","authenticated-orcid":false,"given":"Furqan","family":"Rustam","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, University of Management and Technology, Lahore 544700, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8747-5679","authenticated-orcid":false,"given":"Emmanuel Soriano","family":"Flores","sequence":"additional","affiliation":[{"name":"Higher Polytechnic School, Universidad Europea del Atl\u00e1ntico (UNEATLANTICO), Isabel Torres 21, 39011 Santander, Spain"},{"name":"Department of Project Management, Universidad Internacional Iberoamericana (UNINI-MX), Campeche 24560, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0982-815X","authenticated-orcid":false,"given":"Juan","family":"Lu\u00eds Vidal Maz\u00f3n","sequence":"additional","affiliation":[{"name":"Higher Polytechnic School, Universidad Europea del Atl\u00e1ntico (UNEATLANTICO), Isabel Torres 21, 39011 Santander, Spain"},{"name":"Project Department, Universidade Internacional do Cuanza, Municipio do Kuito, Bairro Sede, EN250, Bi\u00e9, Angola"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3134-7720","authenticated-orcid":false,"given":"Isabel","family":"de la Torre Diez","sequence":"additional","affiliation":[{"name":"Department of Signal Theory and Communications and Telematic Engineering, University of Valladolid, Paseo de Bel\u00e9n 15, 47011 Valladolid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8271-6496","authenticated-orcid":false,"given":"Imran","family":"Ashraf","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,16]]},"reference":[{"key":"ref_1","unstructured":"Korshunov, P., and Marcel, S. (2018). Deepfakes: A new threat to face recognition? assessment and detection. arXiv."},{"key":"ref_2","first-page":"4","article-title":"Deepfakes: How a pervert shook the world","volume":"4","author":"Chawla","year":"2019","journal-title":"Int. J. Adv. Res. Dev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1177\/1365712718807226","article-title":"Determining authenticity of video evidence in the age of artificial intelligence and in the wake of deepfake videos","volume":"23","author":"Maras","year":"2019","journal-title":"Int. J. Evid. Proof"},{"key":"ref_4","unstructured":"Kingma, D.P., and Welling, M. (2014). Auto-Encoding Variational Bayes. arXiv."},{"key":"ref_5","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. Adv. Neural Inf. Process. Syst., 27."},{"key":"ref_6","first-page":"1753","article-title":"Deep fakes: A looming challenge for privacy, democracy, and national security","volume":"107","author":"Chesney","year":"2019","journal-title":"Calif. L. Rev."},{"key":"ref_7","first-page":"105","article-title":"Pornographic Deepfakes: The Case for Federal Criminalization of Revenge Porn\u2019s Next Tragic Act","volume":"14","author":"Delfino","year":"2020","journal-title":"Actual Probs. Econ. L."},{"key":"ref_8","first-page":"35","article-title":"Deepfakes-I More Highten Iling Than Photoshop on Steroids","volume":"58","author":"Dixon","year":"2019","journal-title":"Judges\u2019 J."},{"key":"ref_9","unstructured":"Feldstein, S. (2021, September 09). How Artificial Intelligence Systems Could Threaten Democracy. Available online: https:\/\/carnegieendowment.org\/2019\/04\/24\/how-artificial-intelligence-systems-could-threaten-democracy-pub-78984."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ismail, A., Elpeltagy, M., Zaki, M.S., and Eldahshan, K. (2021). A New Deep Learning-Based Methodology for Video Deepfake Detection Using XGBoost. Sensors, 21.","DOI":"10.3390\/s21165413"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1109\/MCSE.2018.2874117","article-title":"The future of misinformation","volume":"21","author":"Day","year":"2019","journal-title":"Comput. Sci. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1353\/tj.2018.0097","article-title":"Deepfakes, artificial intelligence, and some kind of dystopia: The new faces of online post-fact performance","volume":"70","author":"Fletcher","year":"2018","journal-title":"Theatre J."},{"key":"ref_13","unstructured":"(2021, September 09). What Are Deepfakes and Why the Future of Porn is Terrifying Highsnobiety. Available online: https:\/\/www.highsnobiety.com\/p\/what-are-deepfakes-ai-porn\/."},{"key":"ref_14","unstructured":"Roose, K. (The New York Times, 2018). Here come the fake videos, too, The New York Times."},{"key":"ref_15","unstructured":"(2021, September 09). Twitter, Pornhub and Other Platforms Ban AI-Generated Celebrity Porn. Available online: https:\/\/thenextweb.com\/news\/twitter-pornhub-and-other-platforms-ban-ai-generated-celebrity-porn."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"41596","DOI":"10.1109\/ACCESS.2019.2905689","article-title":"Combating deepfake videos using blockchain and smart contracts","volume":"7","author":"Hasan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/MITP.2019.2910503","article-title":"Using blockchain to rein in the new post-truth world and check the spread of fake news","volume":"21","author":"Qayyum","year":"2019","journal-title":"IT Prof."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1002\/mar.21349","article-title":"Online users\u2019 attitudes toward fake news: Implications for brand management","volume":"37","author":"Tiago","year":"2020","journal-title":"Psychol. Mark."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.procs.2018.10.171","article-title":"Detecting fake news in social media networks","volume":"141","author":"Aldwairi","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.chb.2017.11.034","article-title":"Third person effects of fake news: Fake news regulation and media literacy interventions","volume":"80","author":"Jang","year":"2018","journal-title":"Comput. Hum. Behav."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1016\/j.procs.2017.11.106","article-title":"The current state of fake news: Challenges and opportunities","volume":"121","author":"Figueira","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Anderson, K.E. (2018). Getting Acquainted with Social Networks and Apps: Combating Fake News on Social Media, Emerald Group Publishing Limited. Library Hi Tech News.","DOI":"10.1108\/LHTN-02-2018-0010"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3309699","article-title":"The web of false information: Rumors, fake news, hoaxes, clickbait, and various other shenanigans","volume":"11","author":"Zannettou","year":"2019","journal-title":"J. Data Inf. Qual."},{"key":"ref_24","first-page":"16","article-title":"The role of beliefs and behavior on facebook: A semiotic approach to algorithms, fake news, and transmedia journalism","volume":"13","author":"Borges","year":"2019","journal-title":"Int. J. Commun."},{"key":"ref_25","unstructured":"Nguyen, T.T., Nguyen, C.M., Nguyen, D.T., Nguyen, D.T., and Nahavandi, S. (2019). Deep learning for deepfakes creation and detection: A survey. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.inffus.2020.06.014","article-title":"Deepfakes and beyond: A survey of face manipulation and fake detection","volume":"64","author":"Tolosana","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Juefei-Xu, F., Wang, R., Huang, Y., Guo, Q., Ma, L., and Liu, Y. (2021). Countering malicious deepfakes: Survey, battleground, and horizon. arXiv.","DOI":"10.1007\/s11263-022-01606-8"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3425780","article-title":"The creation and detection of deepfakes: A survey","volume":"54","author":"Mirsky","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Deshmukh, A., and Wankhade, S.B. (2021). Deepfake Detection Approaches Using Deep Learning: A Systematic Review. Intelligent Computing and Networking, Springer.","DOI":"10.1007\/978-981-15-7421-4_27"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.intell.2017.10.005","article-title":"\u2018Fake news\u2019: Incorrect, but hard to correct. The role of cognitive ability on the impact of false information on social impressions","volume":"65","author":"Roets","year":"2017","journal-title":"Intelligence"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"104957","DOI":"10.1016\/j.compbiomed.2021.104957","article-title":"Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy","volume":"139","author":"Alamoodi","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"114155","DOI":"10.1016\/j.eswa.2020.114155","article-title":"Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review","volume":"167","author":"Alamoodi","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"103347","DOI":"10.1016\/j.csi.2019.04.006","article-title":"Ten years of visualization of business process models: A systematic literature review","volume":"66","author":"Dani","year":"2019","journal-title":"Comput. Stand. Interfaces"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"105906","DOI":"10.1016\/j.ijsu.2021.105906","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"88","author":"Page","year":"2021","journal-title":"Int. J. Surg."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.jclinepi.2021.02.003","article-title":"Updating guidance for reporting systematic reviews: Development of the PRISMA 2020 statement","volume":"134","author":"Page","year":"2021","journal-title":"J. Clin. Epidemiol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1002\/jrsm.1535","article-title":"Introduction to PRISMA 2020 and implications for research synthesis methodologists","volume":"13","author":"Page","year":"2022","journal-title":"Res. Synth. Methods"},{"key":"ref_37","unstructured":"Keele, S. (2007). Guidelines for Performing Systematic Literature Reviews in Software Engineering, Citeseer. Available online: http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download;jsessionid=61C69CBE81D5F823599F0B65EB89FD3B?doi=10.1.1.117.471&rep=rep1&type=pdf."},{"key":"ref_38","unstructured":"(2021, September 09). Faceswap: Deepfakes Software for All. Available online: https:\/\/github.com\/deepfakes\/faceswap."},{"key":"ref_39","unstructured":"(2021, September 09). FakeApp 2.2.0. Available online: https:\/\/www.malavida.com\/en\/soft\/fakeapp\/."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42979-021-00495-x","article-title":"A Machine Learning Based Approach for Deepfake Detection in Social Media Through Key Video Frame Extraction","volume":"2","author":"Mitra","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_41","unstructured":"Perov, I., Gao, D., Chervoniy, N., Liu, K., Marangonda, S., Um\u00e9, C., Dpfks, M., Facenheim, C.S., RP, L., and Jiang, J. (2020). Deepfacelab: A simple, flexible and extensible face swapping framework. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Bulat, A., and Tzimiropoulos, G. (2017, January 22\u201329). How far are we from solving the 2d & 3d face alignment problem? (and a dataset of 230,000 3d facial landmarks). Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.116"},{"key":"ref_43","unstructured":"Iglovikov, V., and Shvets, A. (2018). Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv."},{"key":"ref_44","unstructured":"(2021, September 09). DFaker. Available online: https:\/\/github.com\/dfaker\/df."},{"key":"ref_45","unstructured":"(2021, September 09). DeepFake tf: Deepfake Based on Tensorflow. Available online: https:\/\/github.com\/StromWine\/DeepFaketf."},{"key":"ref_46","unstructured":"(2021, September 09). Faceswap-GAN. Available online: https:\/\/github.com\/shaoanlu\/faceswap-GAN."},{"key":"ref_47","unstructured":"(2021, September 09). Keras-VGGFace: VGGFace Implementation with Keras Framework. Available online: https:\/\/github.com\/rcmalli\/keras-vggface."},{"key":"ref_48","unstructured":"(2021, September 09). FaceNet. Available online: https:\/\/github.com\/davidsandberg\/facenet."},{"key":"ref_49","unstructured":"(2021, September 09). CycleGAN. Available online: https:\/\/github.com\/junyanz\/pytorch-CycleGAN-and-pix2pix."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Bahdanau, D., and Bengio, Y. (2014). On the properties of neural machine translation: Encoder-decoder approaches. arXiv.","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref_52","unstructured":"BM, N. (2021, September 09). What is an Encoder Decoder Model?. Available online: https:\/\/towardsdatascience.com\/what-is-an-encoder-decoder-model-86b3d57c5e1a."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Lu, L., Zhang, X., Cho, K., and Renals, S. (2015, January 6\u201310). A study of the recurrent neural network encoder-decoder for large vocabulary speech recognition. Proceedings of the Sixteenth Annual Conference of the International Speech Communication Association, Dresden, Germany.","DOI":"10.21437\/Interspeech.2015-654"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., and Han, B. (2015, January 7\u201313). Learning deconvolution network for semantic segmentation. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref_55","unstructured":"Badrinarayanan, V., Kendall, A., and Cipolla, R. (2021, September 09). 1SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. Available online: https:\/\/arxiv.org\/pdf\/1511.00561.pdf."},{"key":"ref_56","unstructured":"Siddiqui, K.A. (2021, September 09). What is an Encoder\/Decoder in Deep Learning?. Available online: https:\/\/www.quora.com\/What-is-an-Encoder-Decoder-in-Deep-Learning."},{"key":"ref_57","unstructured":"Nirkin, Y., Keller, Y., and Hassner, T. (November, January 27). Fsgan: Subject agnostic face swapping and reenactment. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Deng, Y., Yang, J., Chen, D., Wen, F., and Tong, X. (2020, January 13\u201319). Disentangled and controllable face image generation via 3d imitative-contrastive learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00520"},{"key":"ref_59","unstructured":"Li, L., Bao, J., Yang, H., Chen, D., and Wen, F. (2019). Faceshifter: Towards high fidelity and occlusion aware face swapping. arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Lattas, A., Moschoglou, S., Gecer, B., Ploumpis, S., Triantafyllou, V., Ghosh, A., and Zafeiriou, S. (2020, January 13\u201319). AvatarMe: Realistically Renderable 3D Facial Reconstruction \u201cIn-the-Wild\u201d. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00084"},{"key":"ref_61","unstructured":"Chan, C., Ginosar, S., Zhou, T., and Efros, A.A. (November, January 27). Everybody Dance Now. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Haliassos, A., Vougioukas, K., Petridis, S., and Pantic, M. (2021, January 20\u201325). Lips Don\u2019t Lie: A Generalisable and Robust Approach To Face Forgery Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00500"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., and Yu, N. (2021, January 20\u201325). Multi-attentional deepfake detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00222"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Zhao, L., Zhang, M., Ding, H., and Cui, X. (2021). MFF-Net: Deepfake Detection Network Based on Multi-Feature Fusion. Entropy, 23.","DOI":"10.3390\/e23121692"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Hubens, N., Mancas, M., Gosselin, B., Preda, M., and Zaharia, T. (2021, January 1\u20135). Fake-buster: A lightweight solution for deepfake detection. Proceedings of the Applications of Digital Image Processing XLIV, International Society for Optics and Photonics, San Diego, CA, USA.","DOI":"10.1117\/12.2596317"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Siegel, D., Kraetzer, C., Seidlitz, S., and Dittmann, J. (2021). Media Forensics Considerations on DeepFake Detection with Hand-Crafted Features. J. Imaging, 7.","DOI":"10.3390\/jimaging7070108"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"4990","DOI":"10.1002\/int.22499","article-title":"A lightweight 3D convolutional neural network for deepfake detection","volume":"36","author":"Liu","year":"2021","journal-title":"Int. J. Intell. Syst."},{"key":"ref_68","first-page":"1755","article-title":"Dlib-ml: A machine learning toolkit","volume":"10","author":"King","year":"2009","journal-title":"J. Mach. Learn. Res."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","article-title":"Joint face detection and alignment using multitask cascaded convolutional networks","volume":"23","author":"Zhang","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1080\/02564602.2020.1782274","article-title":"Image inpainting detection based on multi-task deep learning network","volume":"38","author":"Wang","year":"2021","journal-title":"IETE Tech. Rev."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Malolan, B., Parekh, A., and Kazi, F. (2020, January 9\u201312). Explainable deep-fake detection using visual interpretability methods. Proceedings of the 2020 3rd International Conference on Information and Computer Technologies (ICICT), San Jose, CA, USA.","DOI":"10.1109\/ICICT50521.2020.00051"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Kharbat, F.F., Elamsy, T., Mahmoud, A., and Abdullah, R. (2019, January 3\u20137). Image feature detectors for deepfake video detection. Proceedings of the 2019 IEEE\/ACS 16th International Conference on Computer Systems and Applications (AICCSA), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/AICCSA47632.2019.9035360"},{"key":"ref_73","unstructured":"Li, Y., and Lyu, S. (2018). Exposing deepfake videos by detecting face warping artifacts. arXiv."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Yang, X., Li, Y., and Lyu, S. (2019, January 12\u201317). Exposing deep fakes using inconsistent head poses. Proceedings of the ICASSP 2019\u20142019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8683164"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"G\u00fcera, D., and Delp, E.J. (2018, January 27\u201330). Deepfake video detection using recurrent neural networks. Proceedings of the 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Auckland, New Zealand.","DOI":"10.1109\/AVSS.2018.8639163"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Chen, P., Liu, J., Liang, T., Zhou, G., Gao, H., Dai, J., and Han, J. (2020, January 6\u201310). Fsspotter: Spotting face-swapped video by spatial and temporal clues. Proceedings of the 2020 IEEE International Conference on Multimedia and Expo (ICME), London, UK.","DOI":"10.1109\/ICME46284.2020.9102914"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Ranjan, P., Patil, S., and Kazi, F. (2020, January 9\u201312). Improved Generalizability of Deep-Fakes Detection using Transfer Learning Based CNN Framework. Proceedings of the 2020 3rd International Conference on Information and Computer Technologies (ICICT), San Jose, CA, USA.","DOI":"10.1109\/ICICT50521.2020.00021"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Jafar, M.T., Ababneh, M., Al-Zoube, M., and Elhassan, A. (2020, January 7\u20139). Forensics and analysis of deepfake videos. Proceedings of the 2020 11th International Conference on Information and Communication Systems (ICICS), Irbid, Jordan.","DOI":"10.1109\/ICICS49469.2020.239493"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Li, Y., Chang, M.C., and Lyu, S. (2018, January 11\u201313). In ictu oculi: Exposing ai created fake videos by detecting eye blinking. Proceedings of the 2018 IEEE International Workshop on Information Forensics and Security (WIFS), Hong Kong, China.","DOI":"10.1109\/WIFS.2018.8630787"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"83144","DOI":"10.1109\/ACCESS.2020.2988660","article-title":"DeepVision: Deepfakes detection using human eye blinking pattern","volume":"8","author":"Jung","year":"2020","journal-title":"IEEE Access"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Siddiqui, H.U.R., Shahzad, H.F., Saleem, A.A., Khan Khakwani, A.B., Rustam, F., Lee, E., Ashraf, I., and Dudley, S. (2021). Respiration Based Non-Invasive Approach for Emotion Recognition Using Impulse Radio Ultra Wide Band Radar and Machine Learning. Sensors, 21.","DOI":"10.3390\/s21248336"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"6626974","DOI":"10.1155\/2021\/6626974","article-title":"Countering Spoof: Towards Detecting Deepfake with Multidimensional Biological Signals","volume":"2021","author":"Jin","year":"2021","journal-title":"Secur. Commun. Netw."},{"key":"ref_83","unstructured":"Ciftci, U.A., Demir, I., and Yin, L. (2020). Fakecatcher: Detection of synthetic portrait videos using biological signals. IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"2878","DOI":"10.1109\/TBME.2013.2266196","article-title":"Robust Pulse Rate From Chrominance-Based rPPG","volume":"60","author":"Jeanne","year":"2013","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Zhao, C., Lin, C.L., Chen, W., and Li, Z. (2018, January 18\u201322). A Novel Framework for Remote Photoplethysmography Pulse Extraction on Compressed Videos. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00177"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1109\/TCSVT.2014.2364415","article-title":"Motion-Resistant Remote Imaging Photoplethysmography Based on the Optical Properties of Skin","volume":"25","author":"Feng","year":"2015","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1364\/BOE.9.000873","article-title":"Bounded Kalman filter method for motion-robust, non-contact heart rate estimation","volume":"2","author":"Prakash","year":"2018","journal-title":"Biomed. Opt. Express"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Tulyakov, S., Alameda-Pineda, X., Ricci, E., Yin, L., Cohn, J.F., and Sebe, N. (2016, January 27\u201330). Self-Adaptive Matrix Completion for Heart Rate Estimation from Face Videos under Realistic Conditions. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.263"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Demir, I., and Ciftci, U.A. (2021). Where Do Deep Fakes Look? Synthetic Face Detection via Gaze Tracking. ACM Symposium on Eye Tracking Research and Applications, Association for Computing Machinery. ETRA \u201921 Full Papers.","DOI":"10.1145\/3448017.3457387"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Ciftci, U.A., Demir, I., and Yin, L. (28\u20131, January 28). How do the hearts of deep fakes beat? deep fake source detection via interpreting residuals with biological signals. Proceedings of the 2020 IEEE international joint conference on biometrics (IJCB), Houston, TX, USA.","DOI":"10.1109\/IJCB48548.2020.9304909"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Guarnera, L., Giudice, O., and Battiato, S. (2020, January 14\u201319). Deepfake detection by analyzing convolutional traces. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00341"},{"key":"ref_92","unstructured":"Corcoran, M., and Henry, M. (2021, August 12). The Tom Cruise deepfake that set off \u2018terror\u2019 in the heart of Washington DC. Available online: https:\/\/www.abc.net.au\/news\/2021-06-24\/tom-cruise-deepfake-chris-ume-security-washington-dc\/100234772."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1109\/JSTSP.2020.3007250","article-title":"Ganprintr: Improved fakes and evaluation of the state of the art in face manipulation detection","volume":"14","author":"Neves","year":"2020","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Dang, H., Liu, F., Stehouwer, J., Liu, X., and Jain, A.K. (2020, January 13\u201319). On the detection of digital face manipulation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00582"},{"key":"ref_95","first-page":"216","article-title":"Application of geometry to rgb images for facial landmark localisation-a preliminary approach","volume":"8","author":"Vezzetti","year":"2016","journal-title":"Int. J. Biom."},{"key":"ref_96","unstructured":"Wang, S.Y., Wang, O., Owens, A., Zhang, R., and Efros, A.A. (November, January 27). Detecting photoshopped faces by scripting photoshop. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Tariq, S., Lee, S., Kim, H., Shin, Y., and Woo, S.S. (2018, January 15). Detecting both machine and human created fake face images in the wild. Proceedings of the 2nd International Workshop on Multimedia Privacy and Security, Toronto, ON, Canada.","DOI":"10.1145\/3267357.3267367"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Matern, F., Riess, C., and Stamminger, M. (2019, January 7\u201311). Exploiting visual artifacts to expose deepfakes and face manipulations. Proceedings of the 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW), Waikoloa Village, HA, USA.","DOI":"10.1109\/WACVW.2019.00020"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1903","DOI":"10.1109\/TIFS.2016.2561898","article-title":"Detecting facial retouching using supervised deep learning","volume":"11","author":"Bharati","year":"2016","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., and Guo, B. (2020, January 13\u201319). Face x-ray for more general face forgery detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00505"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Reimao, R., and Tzerpos, V. (2019, January 10\u201312). FoR: A Dataset for Synthetic Speech Detection. Proceedings of the 2019 International Conference on Speech Technology and Human-Computer Dialogue (SpeD), Timisoara, Romania.","DOI":"10.1109\/SPED.2019.8906599"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"106503","DOI":"10.1016\/j.dib.2020.106503","article-title":"Ar-DAD: Arabic diversified audio dataset","volume":"33","author":"Lataifeh","year":"2020","journal-title":"Data Brief"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"105331","DOI":"10.1016\/j.dib.2020.105331","article-title":"A dataset of histograms of original and fake voice recordings (H-Voice)","volume":"29","author":"Ballesteros","year":"2020","journal-title":"Data Brief"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Wu, Z., Kinnunen, T., Evans, N., Yamagishi, J., Hanil\u00e7i, C., and Sahidullah, M. (2021, September 04). ASVspoof 2015: The First Automatic Speaker Verification Spoofing and Countermeasures Challenge. Available online: https:\/\/www.researchgate.net\/publication\/279448325_ASVspoof_2015_the_First_Automatic_Speaker_Verification_Spoofing_and_Countermeasures_Challenge.","DOI":"10.21437\/Interspeech.2015-462"},{"key":"ref_105","unstructured":"Kinnunen, T., Sahidullah, M., Delgado, H., Todisco, M., Evans, N., Yamagishi, J., and Lee, K.A. (2021, September 14). The 2nd Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2017) Database, Version 2. Available online: https:\/\/erepo.uef.fi\/handle\/123456789\/7184."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Todisco, M., Wang, X., Vestman, V., Sahidullah, M., Delgado, H., Nautsch, A., Yamagishi, J., Evans, N., Kinnunen, T., and Lee, K.A. (2019). ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection. arXiv.","DOI":"10.21437\/Interspeech.2019-2249"},{"key":"ref_107","unstructured":"Rodr\u00edguez-Ortega, Y., Ballesteros, D.M., and Renza, D. (2019, January 7\u20139). A machine learning model to detect fake voice. Proceedings of the International Conference on Applied Informatics, Madrid, Spain."},{"key":"ref_108","unstructured":"Bhatia, K., Agrawal, A., Singh, P., and Singh, A.K. (2022). Detection of AI Synthesized Hindi Speech. arXiv."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Borrelli, C., Bestagini, P., Antonacci, F., Sarti, A., and Tubaro, S. (2021). Synthetic Speech Detection Through Short-Term and Long-Term Prediction Traces. EURASIP J. Inform. Security, 1\u201314.","DOI":"10.1186\/s13635-021-00116-3"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Liu, T., Yan, D., Wang, R., Yan, N., and Chen, G. (2021). Identification of Fake Stereo Audio Using SVM and CNN. Information, 12.","DOI":"10.3390\/info12070263"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Figueroa-Garc\u00eda, J.C., D\u00edaz-Gutierrez, Y., Gaona-Garc\u00eda, E.E., and Orjuela-Ca\u00f1\u00f3n, A.D. (2021). Fake Speech Recognition Using Deep Learning. Applied Computer Sciences in Engineering, Springer International Publishing.","DOI":"10.1007\/978-3-030-86702-7"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Wang, R., Juefei-Xu, F., Huang, Y., Guo, Q., Xie, X., Ma, L., and Liu, Y. (2020, January 12\u201316). DeepSonar: Towards Effective and Robust Detection of AI-Synthesized Fake Voices. Proceedings of the 28th ACM International Conference on Multimedia, Seattle, WA, USA.","DOI":"10.1145\/3394171.3413716"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.neucom.2020.07.099","article-title":"Arabic audio clips: Identification and discrimination of authentic Cantillations from imitations","volume":"418","author":"Lataifeh","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Fagni, T., Falchi, F., Gambini, M., Martella, A., and Tesconi, M. (2021). TweepFake: About detecting deepfake tweets. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0251415"},{"key":"ref_115","unstructured":"Sanderson, C. (2021, September 09). VidTIMIT Audio-Video Dataset. Available online: https:\/\/zenodo.org\/record\/158963\/export\/xm#.Yqf3q-xByUk."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Li, Y., Yang, X., Sun, P., Qi, H., and Lyu, S. (2020, January 13\u201319). Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics. Proceedings of the IEEE Conference on Computer Vision and Patten Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00327"},{"key":"ref_117","unstructured":"idiap (2021, September 09). DEEPFAKETIMIT. Available online: https:\/\/www.idiap.ch\/en\/dataset\/deepfaketimit."},{"key":"ref_118","unstructured":"LYTIC (2021, September 09). FaceForensics++. Available online: https:\/\/www.kaggle.com\/datasets\/sorokin\/faceforensics."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Li, Y., Chang, M.C., and Lyu, S. (2018). In ictu oculi: Exposing ai generated fake face videos by detecting eye blinking. arXiv.","DOI":"10.1109\/WIFS.2018.8630787"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/12\/4556\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:33:17Z","timestamp":1760139197000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/12\/4556"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,16]]},"references-count":119,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["s22124556"],"URL":"https:\/\/doi.org\/10.3390\/s22124556","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,16]]}}}