{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T17:36:46Z","timestamp":1772041006489,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T00:00:00Z","timestamp":1749427200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Instituto de Ci\u00eancia e Tecnologia Ita\u00fa\u2014ICTi"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Deepfake images, synthetic images created using digital software, continue to present a serious threat to online platforms. This is especially relevant for biometric verification systems, as deepfakes that attempt to bypass such measures increase the risk of impersonation, identity theft and scams. Although research on deepfake image detection has provided many high-performing classifiers, many of these commonly used detection models lack generalizability across different methods of deepfake generation. For companies and governments fighting identify fraud, a lack of generalization is challenging, as malicious actors may use a variety of deepfake image-generation methods available through online wrappers. This work explores if combining multiple classifiers into an ensemble model can improve generalization without losing performance across different generation methods. It also considers current methods of deepfake image generation, with a focus on publicly available and easily accessible methods. We compare our framework against its underlying models to show how companies can better respond to emerging deepfake generation methods.<\/jats:p>","DOI":"10.3390\/computers14060225","type":"journal-article","created":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T05:54:05Z","timestamp":1749448445000},"page":"225","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Ensemble-Based Biometric Verification: Defending Against Multi-Strategy Deepfake Image Generation"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1990-9417","authenticated-orcid":false,"given":"Hilary","family":"Zen","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1044-2792","authenticated-orcid":false,"given":"Rohan","family":"Wagh","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0039-9875","authenticated-orcid":false,"given":"Miguel","family":"Wanderley","sequence":"additional","affiliation":[{"name":"Instituto de Ci\u00eancia e Tecnologia Ita\u00fa -ICTi, Av. Engenheiro Armando de Arruda Pereira 774, Jabaquara - S\u00e3o Paulo, S\u00e3o Paulo 04308-000, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6508-6812","authenticated-orcid":false,"given":"Gustavo","family":"Bicalho","sequence":"additional","affiliation":[{"name":"Instituto de Ci\u00eancia e Tecnologia Ita\u00fa -ICTi, Av. Engenheiro Armando de Arruda Pereira 774, Jabaquara - S\u00e3o Paulo, S\u00e3o Paulo 04308-000, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7940-6452","authenticated-orcid":false,"given":"Rachel","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2021-5702","authenticated-orcid":false,"given":"Megan","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8963-5074","authenticated-orcid":false,"given":"Rafael","family":"Palacios","sequence":"additional","affiliation":[{"name":"Cybersecurity at MIT Sloan (CAMS), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA"},{"name":"Institute for Research in Technology, Universidad Pontificia Comillas, Alberto Aguilera 23, 28015 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3086-3985","authenticated-orcid":false,"given":"Lucas","family":"Carvalho","sequence":"additional","affiliation":[{"name":"Instituto de Ci\u00eancia e Tecnologia Ita\u00fa -ICTi, Av. Engenheiro Armando de Arruda Pereira 774, Jabaquara - S\u00e3o Paulo, S\u00e3o Paulo 04308-000, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0758-9802","authenticated-orcid":false,"given":"Guilherme","family":"Rinaldo","sequence":"additional","affiliation":[{"name":"Instituto de Ci\u00eancia e Tecnologia Ita\u00fa -ICTi, Av. 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Computers, 12.","DOI":"10.3390\/computers12100216"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"100538","DOI":"10.1016\/j.chbr.2024.100538","article-title":"Human performance in detecting deepfakes: A systematic review and meta-analysis of 56 papers","volume":"16","author":"Diel","year":"2024","journal-title":"Comput. Hum. Behav. Rep."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Salvi, D., Liu, H., Mandelli, S., Bestagini, P., Zhou, W., Zhang, W., and Tubaro, S. (2023). A Robust Approach to Multimodal Deepfake Detection. J. Imaging, 9.","DOI":"10.3390\/jimaging9060122"},{"key":"ref_4","unstructured":"Khalid, H., Tariq, S., Kim, M., and Woo, S.S. (2021). FakeAVCeleb: A novel audio-video multimodal deepfake dataset. arXiv."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Khalid, H., Kim, M., Tariq, S., and Woo, S.S. (2021, January 24). Evaluation of an Audio-Video Multimodal Deepfake Dataset using Unimodal and Multimodal Detectors. Proceedings of the 1st Workshop on Synthetic Multimedia\u2014Audiovisual Deepfake Generation and Detection, ADGD\u201921, New York, NY, USA.","DOI":"10.1145\/3476099.3484315"},{"key":"ref_6","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_7","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_8","unstructured":"Newall, M., and Deeney, C. (2025, June 01). Nearly 1 in 3 Americans Report Being a Victim of Online Financial Fraud or Cybercrime. Available online: https:\/\/www.ipsos.com\/en-us\/nearly-1-3-americans-report-being-victim-online-financial-fraud-or-cybercrime."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Korshunov, P., and Marcel, S. (2019, January 4\u20137). Vulnerability assessment and detection of deepfake videos. Proceedings of the 2019 International Conference on Biometrics (ICB), Crete, Greece.","DOI":"10.1109\/ICB45273.2019.8987375"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jain, A., Ross, A., and Nandakumar, K. (2011). Introduction to Biometrics, Springer.","DOI":"10.1007\/978-0-387-77326-1"},{"key":"ref_11","unstructured":"Pei, G., Zhang, J., Hu, M., Zhang, Z., Wang, C., Wu, Y., Zhai, G., Yang, J., Shen, C., and Tao, D. (2024). Deepfake generation and detection: A benchmark and survey. arXiv."},{"key":"ref_12","unstructured":"CSA Top Threats Working Group (2024). Top Threats to Cloud Computing 2024, Cloud Security Alliance. Technical Report."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yadav, D., and Salmani, S. (2019, January 15\u201317). Deepfake: A Survey on Facial Forgery Technique Using Generative Adversarial Network. Proceedings of the 2019 International Conference on Intelligent Computing and Control Systems (ICCS), Madurai, India.","DOI":"10.1109\/ICCS45141.2019.9065881"},{"key":"ref_14","unstructured":"Palesi, M., Trajkovic, L., Jayakumari, J., and Jose, J. (2019, January 23\u201325). Detection of Deepfake Images Created Using Generative Adversarial Networks: A Review. Proceedings of the Second International Conference on Networks and Advances in Computational Technologies, Thiruvananthapuram, India."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2153","DOI":"10.1016\/j.procs.2023.01.191","article-title":"A GAN-Based Model of Deepfake Detection in Social Media","volume":"218","author":"Preeti","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1561\/2200000056","article-title":"An introduction to variational autoencoders","volume":"12","author":"Kingma","year":"2019","journal-title":"Found. Trends\u00ae Mach. Learn."},{"key":"ref_17","unstructured":"Dehghani, A., and Saberi, H. (2025). Generating and Detecting Various Types of Fake Image and Audio Content: A Review of Modern Deep Learning Technologies and Tools. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Khalid, H., and Woo, S.S. (2020, January 14\u201319). Oc-fakedect: Classifying deepfakes using one-class variational autoencoder. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00336"},{"key":"ref_19","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_20","unstructured":"Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S. (2015). Deep unsupervised learning using nonequilibrium thermodynamics. In Proceedings of the International conference on machine learning. arXiv."},{"key":"ref_21","unstructured":"Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., and Poole, B. (2020). Score-based generative modeling through stochastic differential equations. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3059","DOI":"10.1007\/s11263-024-02295-1","article-title":"Lavie: High-quality video generation with cascaded latent diffusion models","volume":"133","author":"Wang","year":"2024","journal-title":"Int. J. Comput. Vis."},{"key":"ref_23","first-page":"8406","article-title":"Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation","volume":"36","author":"Wang","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_24","unstructured":"Song, J., Meng, C., and Ermon, S. (2020). Denoising diffusion implicit models. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2022, January 18\u201324). High-resolution image synthesis with latent diffusion models. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref_26","unstructured":"Sauer, A., Lorenz, D., Blattmann, A., and Rombach, R. (October, January 29). Adversarial diffusion distillation. Proceedings of the European Conference on Computer Vision, Milan, Italy."},{"key":"ref_27","unstructured":"Li, B., Sun, J., and Poskitt, C.M. (2023). How generalizable are deepfake detectors? An empirical study. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, S.Y., Wang, O., Zhang, R., Owens, A., and Efros, A.A. (2020, January 13\u201319). CNN-generated images are surprisingly easy to spot\u2026 for now. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00872"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, Z., Qi, X., and Torr, P.H. (2020, January 13\u201319). Global texture enhancement for fake face detection in the wild. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00808"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ghita, B., Kuzminykh, I., Usama, A., Bakhshi, T., and Marchang, J. (2024, January 24\u201327). Deepfake Image Detection Using Vision Transformer Models. Proceedings of the 2024 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Tbilisi, Georgia.","DOI":"10.1109\/BlackSeaCom61746.2024.10646310"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chai, L., Bau, D., Lim, S.N., and Isola, P. (2020, January 23\u201328). What makes fake images detectable? Understanding properties that generalize. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK. Proceedings, Part XXVI 16.","DOI":"10.1007\/978-3-030-58574-7_7"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"\u021a\u00e2n\u021baru, D.C., Onea\u021b\u0103, E., and Onea\u021b\u0103, D. (2024, January 3\u20138). Weakly-supervised deepfake localization in diffusion-generated images. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV57701.2024.00614"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Asan, J., Ekaputri, I., Natalie, C., and Purwandari, K. (2023, January 1\u20132). Exploring Generative Adversarial Networks (GANs) for Deepfake Detection: A Systematic Literature Review. Proceedings of the 2023 International Workshop on Artificial Intelligence and Image Processing (IWAIIP), Yogyakarta, Indonesia.","DOI":"10.1109\/IWAIIP58158.2023.10462832"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Jheelan, J., and Pudaruth, S. (2025). Using Deep Learning to Identify Deepfakes Created Using Generative Adversarial Networks. Computers, 14.","DOI":"10.3390\/computers14020060"},{"key":"ref_35","unstructured":"Nirkin, Y., Wolf, L., Keller, Y., and Hassner, T. (2020). Deepfake detection based on the discrepancy between the face and its context. arXiv."},{"key":"ref_36","unstructured":"Huang, Z., Hu, J., Li, X., He, Y., Zhao, X., Peng, B., Wu, B., Huang, X., and Cheng, G. (2024). SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model. arXiv."},{"key":"ref_37","unstructured":"Koutlis, C., and Papadopoulos, S. (2024). DiMoDif: Discourse Modality-information Differentiation for Audio-visual Deepfake Detection and Localization. arXiv."},{"key":"ref_38","unstructured":"Zhang, Y., Miao, C., Luo, M., Li, J., Deng, W., Yao, W., Li, Z., Hu, B., Feng, W., and Gong, T. (November, January 28). MFMS: Learning Modality-Fused and Modality-Specific Features for Deepfake Detection and Localization Tasks. Proceedings of the 32nd ACM International Conference on Multimedia, Melburn, Australia."},{"key":"ref_39","unstructured":"Ricker, J., Damm, S., Holz, T., and Fischer, A. (2022). Towards the detection of diffusion model deepfakes. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1109\/34.58871","article-title":"Neural network ensembles","volume":"12","author":"Hansen","year":"1990","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1162\/neco.1991.3.1.79","article-title":"Adaptive Mixtures of Local Experts","volume":"3","author":"Jacobs","year":"1991","journal-title":"Neural Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1117\/1.1526105","article-title":"Feedback-based architecture for reading courtesy amounts on checks","volume":"12","author":"Palacios","year":"2003","journal-title":"J. Electron. Imaging"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Kuncheva, L.I. (2014). Combining Pattern Classifiers: Methods and Algorithms, John Wiley & Sons.","DOI":"10.1002\/9781118914564"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","article-title":"Stacked generalization","volume":"5","author":"Wolpert","year":"1992","journal-title":"Neural Netw."},{"key":"ref_45","first-page":"62","article-title":"A stacked generalization ensemble approach for improved intrusion detection","volume":"18","author":"Oriola","year":"2020","journal-title":"Int. J. Comput. Sci. Inf. Secur."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1007\/s10479-008-0476-1","article-title":"Analysis of stochastic problem decomposition algorithms in computational grids","volume":"166","author":"Latorre","year":"2009","journal-title":"Ann. Oper. Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3702638","article-title":"Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challenges","volume":"57","author":"Dong","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Apostolidis, K.D., and Papakostas, G.A. (2021). A survey on adversarial deep learning robustness in medical image analysis. Electronics, 10.","DOI":"10.3390\/electronics10172132"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"61113","DOI":"10.1109\/ACCESS.2024.3395118","article-title":"How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses","volume":"12","author":"Costa","year":"2024","journal-title":"IEEE Access"},{"key":"ref_50","unstructured":"Song, H., Huang, S., Dong, Y., and Tu, W.W. (2023). Robustness and Generalizability of Deepfake Detection: A Study with Diffusion Models. arXiv."},{"key":"ref_51","unstructured":"R\u00f6ssler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., and Nie\u00dfner, M. (November, January 27). FaceForensics++: Learning to Detect Manipulated Facial Images. Proceedings of the International Conference on Computer Vision (ICCV), Seoul, South Korea."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., and Choo, J. (2018, January 18\u201322). StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00916"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Afchar, D., Nozick, V., Yamagishi, J., and Echizen, I. (2018, January 11\u201313). Mesonet: A compact facial video forgery detection network. Proceedings of the 2018 IEEE International Workshop on Information Forensics and Security (WIFS), Hong Kong, China.","DOI":"10.1109\/WIFS.2018.8630761"}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/6\/225\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:48:41Z","timestamp":1760032121000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/6\/225"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,9]]},"references-count":53,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["computers14060225"],"URL":"https:\/\/doi.org\/10.3390\/computers14060225","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,9]]}}}