{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T10:02:47Z","timestamp":1780567367993,"version":"3.54.1"},"reference-count":40,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T00:00:00Z","timestamp":1665619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"JSPS KAKENHI","doi-asserted-by":"publisher","award":["JP22K04034"],"award-info":[{"award-number":["JP22K04034"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"JSPS KAKENHI","doi-asserted-by":"publisher","award":["2022C-183"],"award-info":[{"award-number":["2022C-183"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"JSPS KAKENHI","doi-asserted-by":"publisher","award":["2021C-589"],"award-info":[{"award-number":["2021C-589"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"JSPS KAKENHI","doi-asserted-by":"publisher","award":["2020C-780"],"award-info":[{"award-number":["2020C-780"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"JSPS KAKENHI","doi-asserted-by":"publisher","award":["2020Q-015"],"award-info":[{"award-number":["2020Q-015"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Waseda University Grant for Special Research Projects","award":["JP22K04034"],"award-info":[{"award-number":["JP22K04034"]}]},{"name":"the Waseda University Grant for Special Research Projects","award":["2022C-183"],"award-info":[{"award-number":["2022C-183"]}]},{"name":"the Waseda University Grant for Special Research Projects","award":["2021C-589"],"award-info":[{"award-number":["2021C-589"]}]},{"name":"the Waseda University Grant for Special Research Projects","award":["2020C-780"],"award-info":[{"award-number":["2020C-780"]}]},{"name":"the Waseda University Grant for Special Research Projects","award":["2020Q-015"],"award-info":[{"award-number":["2020Q-015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recently, the dangers associated with face generation technology have been attracting much attention in image processing and forensic science. The current face anti-spoofing methods based on Generative Adversarial Networks (GANs) suffer from defects such as overfitting and generalization problems. This paper proposes a new generation method using a one-class classification model to judge the authenticity of facial images for the purpose of realizing a method to generate a model that is as compatible as possible with other datasets and new data, rather than strongly depending on the dataset used for training. The method proposed in this paper has the following features: (a) we adopted various filter enhancement methods as basic pseudo-image generation methods for data enhancement; (b) an improved Multi-Channel Convolutional Neural Network (MCCNN) was adopted as the main network, making it possible to accept multiple preprocessed data individually, obtain feature maps, and extract attention maps; (c) as a first ingenuity in training the main network, we augmented the data using weakly supervised learning methods to add attention cropping and dropping to the data; (d) as a second ingenuity in training the main network, we trained it in two steps. In the first step, we used a binary classification loss function to ensure that known fake facial features generated by known GAN networks were filtered out. In the second step, we used a one-class classification loss function to deal with the various types of GAN networks or unknown fake face generation methods. We compared our proposed method with four recent methods. Our experiments demonstrate that the proposed method improves cross-domain detection efficiency while maintaining source-domain accuracy. These studies show one possible direction for improving the correct answer rate in judging facial image authenticity, thereby making a great contribution both academically and practically.<\/jats:p>","DOI":"10.3390\/s22207767","type":"journal-article","created":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T01:44:13Z","timestamp":1665711853000},"page":"7767","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Deep Learning Based One-Class Detection System for Fake Faces Generated by GAN Network"],"prefix":"10.3390","volume":"22","author":[{"given":"Shengyin","family":"Li","sequence":"first","affiliation":[{"name":"Graduate School of Information, Production and Systems, Waseda University, Kitakyushu 808-0135, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9640-4725","authenticated-orcid":false,"given":"Vibekananda","family":"Dutta","sequence":"additional","affiliation":[{"name":"Graduate School of Information, Production and Systems, Waseda University, Kitakyushu 808-0135, Japan"},{"name":"Institute of Micromechanics and Photonics, Faculty of Mechatronics, Warsaw University of Technology, 00-661 Warszawa, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1026-5902","authenticated-orcid":false,"given":"Xin","family":"He","sequence":"additional","affiliation":[{"name":"Graduate School of Information, Production and Systems, Waseda University, Kitakyushu 808-0135, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7776-2984","authenticated-orcid":false,"given":"Takafumi","family":"Matsumaru","sequence":"additional","affiliation":[{"name":"Graduate School of Information, Production and Systems, Waseda University, Kitakyushu 808-0135, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mo, H., Chen, B., and Luo, W. (2018, January 20\u201322). Fake faces identification via convolutional neural network. Proceedings of the 6th ACM Workshop on Information Hiding and Multimedia Security, Innsbruck, Austria.","DOI":"10.1145\/3206004.3206009"},{"key":"ref_2","unstructured":"Cozzolino, D., Thies, J., R\u00f6ssler, A., Riess, C., Nie\u00dfner, M., and Verdoliva, L. (2018). Forensictransfer: Weakly-supervised domain adaptation for forgery detection. arXiv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2772","DOI":"10.1109\/TIFS.2018.2834147","article-title":"Distinguishing between natural and computer-generated images using convolutional neural networks","volume":"13","author":"Quan","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_4","first-page":"2672","article-title":"Generative adversarial nets","volume":"Volume 2","author":"Goodfellow","year":"2014","journal-title":"Proceedings of the 27th International Conference on Neural Information Processing Systems"},{"key":"ref_5","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_7","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_8","unstructured":"Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A. (2019, January 10\u201315). Self-attention generative adversarial networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_9","unstructured":"Brock, A., Donahue, J., and Simonyan, K. (2018). Large scale GAN training for high fidelity natural image synthesis. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Daras, G., Odena, A., Zhang, H., and Dimakis, A.G. (2020, January 13). Your local GAN: Designing two dimensional local attention mechanisms for generative models. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01454"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gao, H., Pei, J., and Huang, H. (2019, January 4\u20138). Progan: Network embedding via proximity generative adversarial network. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330866"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., and Aila, T. (2019, January 15\u201320). A style-based generator architecture for generative adversarial networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref_13","unstructured":"Karnewar, A., and Wang, O. (2020, January 13\u201319). MSG-GAN: Multi-Scale Gradient GAN for Stable Image Synthesis. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., and Aila, T. (2020, January 13\u201319). Analyzing and improving the image quality of stylegan. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Neethirajan, S. (2021). Is Seeing Still Believing? Leveraging Deepfake Technology for Livestock Farming. Front. Vet. Sci., 8.","DOI":"10.3389\/fvets.2021.740253"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1145\/3371409","article-title":"Will deepfakes do deep damage?","volume":"63","author":"Greengard","year":"2019","journal-title":"Commun. ACM"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Marra, F., Gragnaniello, D., Verdoliva, L., and Poggi, G. (2019, January 28\u201330). Do gans leave artificial fingerprints?. Proceedings of the 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), San Jose, CA, USA.","DOI":"10.1109\/MIPR.2019.00103"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Marra, F., Saltori, C., Boato, G., and Verdoliva, L. (2019, January 28\u201330). Incremental learning for the detection and classification of gan-generated images. Proceedings of the 2019 IEEE International Workshop on Information Forensics and Security (WIFS), San Jose, CA, USA.","DOI":"10.1109\/WIFS47025.2019.9035099"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_20","unstructured":"Inoue, H. (2018). Data augmentation by pairing samples for images classification. arXiv."},{"key":"ref_21","unstructured":"Park, S., and Kwak, N. (2016, January 20\u201324). Analysis on the dropout effect in convolutional neural networks. Proceedings of the Asian Conference on Computer Vision, Taipei, Taiwan."},{"key":"ref_22","unstructured":"DeVries, T., and Taylor, G.W. (2017). Improved regularization of convolutional neural networks with cutout. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q.V. (2018). Autoaugment: Learning augmentation policies from data. arXiv.","DOI":"10.1109\/CVPR.2019.00020"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Singh, K.K., and Lee, Y.J. (2017, January 22\u201329). Hide-and-seek: Forcing a network to be meticulous for weakly-supervised object and action localization. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.381"},{"key":"ref_25","unstructured":"Hu, T., Qi, H., Huang, Q., and Lu, Y. (2019). See better before looking closer: Weakly supervised data augmentation network for fine-grained visual classification. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wen, Y., Zhang, K., Li, Z., and Qiao, Y. (2016, January 11\u201314). A discriminative feature learning approach for deep face recognition. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46478-7_31"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/TIFS.2019.2916652","article-title":"Biometric face presentation attack detection with multi-channel convolutional neural network","volume":"15","author":"George","year":"2019","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2884","DOI":"10.1109\/TIFS.2018.2833032","article-title":"A light cnn for deep face representation with noisy labels","volume":"13","author":"Wu","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1109\/TIFS.2020.3013214","article-title":"Learning one class representations for face presentation attack detection using multi-channel convolutional neural networks","volume":"16","author":"George","year":"2020","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_30","unstructured":"Goodfellow, I., Warde-Farley, D., Mirza, M., Courville, A., and Bengio, Y. (2013, January 16\u201321). Maxout networks. Proceedings of the International Conference on Machine Learning, Atlanta, GA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014, January 6\u201312). Visualizing and understanding convolutional networks. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1109\/JSTSP.2020.2994523","article-title":"Gan-generated image detection with self-attention mechanism against gan generator defect","volume":"14","author":"Mi","year":"2020","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_33","first-page":"1218","article-title":"Pixel transposed convolutional networks","volume":"42","author":"Gao","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e3","DOI":"10.23915\/distill.00003","article-title":"Deconvolution and checkerboard artifacts","volume":"1","author":"Odena","year":"2016","journal-title":"Distill"},{"key":"ref_35","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 5999\u20136009."},{"key":"ref_36","unstructured":"Hadsell, R., Chopra, S., and LeCun, Y. (2006, January 17\u201322). Dimensionality reduction by learning an invariant mapping. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), New York, NY, USA."},{"key":"ref_37","first-page":"2481","article-title":"Solving inverse problems with piecewise linear estimators: From Gaussian mixture models to structured sparsity","volume":"21","author":"Yu","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_38","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. (CSUR)"},{"key":"ref_39","unstructured":"Razavi, A., Van den Oord, A., and Vinyals, O. (2019, January 8\u201314). Generating diverse high-fidelity images with vq-vae-2. Proceedings of the 33rd International Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"6098","DOI":"10.1109\/ACCESS.2020.2963933","article-title":"Prognosing human activity using actions forecast and structured database","volume":"8","author":"Dutta","year":"2020","journal-title":"IEEE Access"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/20\/7767\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:53:15Z","timestamp":1760143995000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/20\/7767"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,13]]},"references-count":40,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["s22207767"],"URL":"https:\/\/doi.org\/10.3390\/s22207767","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,13]]}}}