{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T02:29:26Z","timestamp":1781490566816,"version":"3.54.1"},"reference-count":54,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T00:00:00Z","timestamp":1671580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Deepfake technology uses auto-encoders and generative adversarial networks to replace or artificially construct fine-tuned faces, emotions, and sounds. Although there have been significant advancements in the identification of particular fake images, a reliable counterfeit face detector is still lacking, making it difficult to identify fake photos in situations with further compression, blurring, scaling, etc. Deep learning models resolve the research gap to correctly recognize phony images, whose objectionable content might encourage fraudulent activity and cause major problems. To reduce the gap and enlarge the fields of view of the network, we propose a dual input convolutional neural network (DICNN) model with ten-fold cross validation with an average training accuracy of 99.36 \u00b1 0.62, a test accuracy of 99.08 \u00b1 0.64, and a validation accuracy of 99.30 \u00b1 0.94. Additionally, we used \u2019SHapley Additive exPlanations (SHAP) \u2019 as explainable AI (XAI) Shapely values to explain the results and interoperability visually by imposing the model into SHAP. The proposed model holds significant importance for being accepted by forensics and security experts because of its distinctive features and considerably higher accuracy than state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/jimaging9010003","type":"journal-article","created":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T02:06:14Z","timestamp":1671674774000},"page":"3","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Auguring Fake Face Images Using Dual Input Convolution Neural Network"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2551-3163","authenticated-orcid":false,"given":"Mohan","family":"Bhandari","sequence":"first","affiliation":[{"name":"Department of Science and Technology, Samriddhi College, Lokanthali, Bhaktapur 44800, Nepal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1010-7552","authenticated-orcid":false,"given":"Arjun","family":"Neupane","sequence":"additional","affiliation":[{"name":"School of Engineering and Technology, Central Queensland University, Norman Gardens, Rockhampton, QLD 4701, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4107-6784","authenticated-orcid":false,"given":"Saurav","family":"Mallik","sequence":"additional","affiliation":[{"name":"Department of Environmental Health, School of Public Health, Harvard University, Boston, MA 02115, USA"},{"name":"Research Assistant, University of Arizona, Tucson, AZ 85721, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0885-1550","authenticated-orcid":false,"given":"Loveleen","family":"Gaur","sequence":"additional","affiliation":[{"name":"Amity International Business School, Amity University, Noida 201303, India"},{"name":"School of Computer Science, Taylor University, Subang Jaya 47500, Malaysia"},{"name":"Graduate School of Business, University of South Pacific, Suva 1168, Fiji"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1060-6722","authenticated-orcid":false,"given":"Hong","family":"Qin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Tennessee, Chattanooga, TN 37996, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gaur, L., Mallik, S., and Jhanjhi, N.Z. (2022, January 8). Introduction to DeepFake Technologies. Proceedings of the DeepFakes: Creation, Detection, and Impact, New York, NY, USA.","DOI":"10.1201\/9781003231493-1"},{"key":"ref_2","unstructured":"Vairamani, A.D. (2022, January 8). Analyzing DeepFakes Videos by Face Warping Artifacts. Proceedings of the DeepFakes: Creation, Detection, and Impact, New York, NY, USA."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s41095-022-0271-y","article-title":"Attention mechanisms in computer vision: A survey","volume":"8","author":"Guo","year":"2022","journal-title":"Comput. Vis. Media"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3401","DOI":"10.1007\/s10462-021-10093-1","article-title":"Natural language processing for Nepali text: A review","volume":"55","author":"Shahi","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-022-01868-2","article-title":"Monkeypox virus detection using pre-trained deep learning-based approaches","volume":"46","author":"Sitaula","year":"2022","journal-title":"J. Med. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gaur, L., and Sahoo, B.M. (2022). Introduction to Explainable AI and Intelligent Transportation. Explainable Artificial Intelligence for Intelligent Transportation Systems: Ethics and Applications, Springer International Publishing.","DOI":"10.1007\/978-3-031-09644-0"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bhandari, M., Panday, S., Bhatta, C.P., and Panday, S.P. (2022, January 23\u201325). Image Steganography Approach Based Ant Colony Optimization with Triangular Chaotic Map. Proceedings of the 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM), Gautam Buddha Nagar, India.","DOI":"10.1109\/ICIPTM54933.2022.9753917"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, D., Arzhaeva, Y., Devnath, L., Qiao, M., Amirgholipour, S., Liao, Q., McBean, R., Hillhouse, J., Luo, S., and Meredith, D. (December, January 29). Automated Pneumoconiosis Detection on Chest X-Rays Using Cascaded Learning with Real and Synthetic Radiographs. Proceedings of the 2020 Digital Image Computing: Techniques and Applications (DICTA), Melbourne, Australia.","DOI":"10.1109\/DICTA51227.2020.9363416"},{"key":"ref_9","unstructured":"Tran, L., Yin, X., and Liu, X. (2017). Representation Learning by Rotating Your Faces. arXiv, Available online: https:\/\/arxiv.org\/abs\/1705.11136."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3072959.3073640","article-title":"Synthesizing obama: Learning lip sync from audio","volume":"36","author":"Suwajanakorn","year":"2017","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C., and Nie\u00dfner, M. (2016, January 27\u201330). Face2face: Real-time face capture and reenactment of rgb videos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.262"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Dang, H., Liu, F., Stehouwer, J., Liu, X., and Jain, A.K. (2020, January 20\u201325). On the detection of digital face manipulation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR42600.2020.00582"},{"key":"ref_13","unstructured":"Rossler, 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 IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","unstructured":"Li, S., Dutta, V., He, X., and Matsumaru, T. (2022). Deep Learning Based One-Class Detection System for Fake Faces Generated by GAN Network. Sensors, 22.","DOI":"10.3390\/s22207767"},{"key":"ref_16","unstructured":"Wong, A.D. (2022, October 31). BLADERUNNER: Rapid Countermeasure for Synthetic (AI-Generated) StyleGAN Faces. Available online: https:\/\/doi.org\/10.48550\/ARXIV.2210.06587."},{"key":"ref_17","unstructured":"Zotov, E. (2022). StyleGAN-Based Machining Digital Twin for Smart Manufacturing. [Ph.D. Thesis, University of Sheffield]."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., and Aila, T. A Style-Based Generator Architecture for Generative Adversarial Networks. arXiv, 2018.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Fu, J., Li, S., Jiang, Y., Lin, K.Y., Qian, C., Loy, C.C., Wu, W., and Liu, Z. (2022, January 23\u201327). Stylegan-human: A data-centric odyssey of human generation. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-19787-1_1"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Xu, Y., Raja, K., and Pedersen, M. (2022, January 4\u20138). Supervised Contrastive Learning for Generalizable and Explainable DeepFakes Detection. Proceedings of the Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, Waikoloa, HI, USA.","DOI":"10.1109\/WACVW54805.2022.00044"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Fu, Y., Sun, T., Jiang, X., Xu, K., and He, P. (2019, January 19\u201321). Robust GAN-Face Detection Based on Dual-Channel CNN Network. Proceedings of the 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Suzhou, China.","DOI":"10.1109\/CISP-BMEI48845.2019.8965991"},{"key":"ref_22","first-page":"1","article-title":"Classification of Real and Fake Human Faces Using Deep Learning","volume":"6","author":"Salman","year":"2022","journal-title":"Int. J. Acad. Eng. Res. (IJAER)"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zheng, L., and Thing, V.L.L. (2017, January 4\u20136). Automated face swapping and its detection. Proceedings of the 2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP), Singapore.","DOI":"10.1109\/SIPROCESS.2017.8124497"},{"key":"ref_24","unstructured":"Huang, G.B., Ramesh, M., Berg, T., and Learned-Miller, E. (2007). Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments, University of Massachusetts. Technical Report 07-49."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7687","DOI":"10.1007\/s11042-020-10098-y","article-title":"Blind detection of glow-based facial forgery","volume":"80","author":"Guo","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_26","unstructured":"Kingma, D.P., and Dhariwal, P. (2018). Glow: Generative flow with invertible 1x1 convolutions. Adv. Neural Inf. Process. Syst., 31."},{"key":"ref_27","unstructured":"Durall, R., Keuper, M., Pfreundt, F.J., and Keuper, J. (2019). Unmasking deepfakes with simple features. arXiv."},{"key":"ref_28","unstructured":"Karras, T., Aila, T., Laine, S., and Lehtinen, J. (2017). Progressive Growing of GANs for Improved Quality, Stability, and Variation. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Gandhi, A., and Jain, S. (2020, January 19\u201324). Adversarial perturbations fool deepfake detectors. Proceedings of the 2020 International joint conference on neural networks (IJCNN), Glasgow, UK.","DOI":"10.1109\/IJCNN48605.2020.9207034"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"7991","DOI":"10.1007\/s00521-022-06902-5","article-title":"Fake visual content detection using two-stream convolutional neural networks","volume":"34","author":"Yousaf","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Santosh, K., Hegadi, R., and Pal, U. (2021, January 8\u201310). Evaluating Performance of Adam Optimization by Proposing Energy Index. Proceedings of the Recent Trends in Image Processing and Pattern Recognition, University of Malta, Msida, Malta.","DOI":"10.1007\/978-3-031-07005-1"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hu, S., Li, Y., and Lyu, S. (2021, January 6\u201311). Exposing GAN-Generated Faces Using Inconsistent Corneal Specular Highlights. Proceedings of the ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada.","DOI":"10.1109\/ICASSP39728.2021.9414582"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2015","DOI":"10.1167\/jov.21.9.2015","article-title":"Synthetic faces: How perceptually convincing are they?","volume":"21","author":"Nightingale","year":"2021","journal-title":"J. Vis."},{"key":"ref_34","unstructured":"Boulahia, H. (2022). Small Dataset of Real And Fake Human Faces for Model Testing. Kaggle."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_36","unstructured":"LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., and Jackel, L. (1989). Handwritten digit recognition with a back-propagation network. Adv. Neural Inf. Process. Syst., 2."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sun, Y., Zhu, L., Wang, G., and Zhao, F. (2017). Multi-input convolutional neural network for flower grading. J. Electr. Comput. Eng., 2017.","DOI":"10.1155\/2017\/9240407"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.1007\/s00607-021-00928-8","article-title":"Multi-input CNN-GRU based human activity recognition using wearable sensors","volume":"103","author":"Dua","year":"2021","journal-title":"Computing"},{"key":"ref_39","first-page":"1","article-title":"Using a Dual-Input Convolutional Neural Network for Automated Detection of Pediatric Supracondylar Fracture on Conventional Radiography","volume":"55","author":"Choi","year":"2019","journal-title":"Investig. Radiol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5859","DOI":"10.1109\/TCOMM.2021.3085895","article-title":"Dual CNN-Based Channel Estimation for MIMO-OFDM Systems","volume":"69","author":"Jiang","year":"2021","journal-title":"IEEE Trans. Commun."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Naglah, A., Khalifa, F., Khaled, R., Razek, A.A.K.A., and El-Baz, A. (2021, January 13\u201316). Thyroid Cancer Computer-Aided Diagnosis System using MRI-Based Multi-Input CNN Model. Proceedings of the 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Nice, France.","DOI":"10.1109\/ISBI48211.2021.9433841"},{"key":"ref_42","unstructured":"Gaur, L., Bhandari, M., Shikhar, B.S., Nz, J., Shorfuzzaman, M., and Masud, M. (2022). Explanation-Driven HCI Model to Examine the Mini-Mental State for Alzheimer\u2019s Disease. ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Gaur, L., Bhandari, M., Razdan, T., Mallik, S., and Zhao, Z. (2022). Explanation-Driven Deep Learning Model for Prediction of Brain Tumour Status Using MRI Image Data. Front. Genet., 13.","DOI":"10.3389\/fgene.2022.822666"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"106156","DOI":"10.1016\/j.compbiomed.2022.106156","article-title":"Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI","volume":"150","author":"Bhandari","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Bachmaier Winter, L. (2022). Criminal Investigation, Technological Development, and Digital Tools: Where Are We Heading?. Investigating and Preventing Crime in the Digital Era, Springer.","DOI":"10.1007\/978-3-031-13952-9_1"},{"key":"ref_46","unstructured":"Ferreira, J.J., and Monteiro, M. (2021). The human-AI relationship in decision-making: AI explanation to support people on justifying their decisions. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"e1434","DOI":"10.1002\/wfs2.1434","article-title":"Explainable artificial intelligence for digital forensics","volume":"4","author":"Hall","year":"2022","journal-title":"Wiley Interdiscip. Rev. Forensic Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"102632","DOI":"10.1016\/j.fsigen.2021.102632","article-title":"Explainable artificial intelligence in forensics: Realistic explanations for number of contributor predictions of DNA profiles","volume":"56","author":"Veldhuis","year":"2022","journal-title":"Forensic Sci. Int. Genet."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Edwards, T., McCullough, S., Nassar, M., and Baggili, I. (2021, January 6\u20139). On Exploring the Sub-domain of Artificial Intelligence (AI) Model Forensics. Proceedings of the International Conference on Digital Forensics and Cyber Crime, Virtual Event, Singapore.","DOI":"10.1007\/978-3-031-06365-7_3"},{"key":"ref_50","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems 30, Curran Associates, Inc."},{"key":"ref_51","unstructured":"Van Rossum, G., and Drake, F.L. (2009). Python 3 Reference Manual, CreateSpace."},{"key":"ref_52","unstructured":"Gulli, A., and Pal, S. (2017). Deep Learning with Keras, Packt Publishing Ltd."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"61677","DOI":"10.1109\/ACCESS.2018.2874767","article-title":"Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications","volume":"6","author":"Carneiro","year":"2018","journal-title":"IEEE Access"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"108319","DOI":"10.1016\/j.dib.2022.108319","article-title":"Representations of machine vision technologies in artworks, games and narratives: A dataset","volume":"42","author":"Rettberg","year":"2022","journal-title":"Data Brief"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/9\/1\/3\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:47:08Z","timestamp":1760147228000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/9\/1\/3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,21]]},"references-count":54,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["jimaging9010003"],"URL":"https:\/\/doi.org\/10.3390\/jimaging9010003","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,21]]}}}