{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T13:03:09Z","timestamp":1780578189837,"version":"3.54.1"},"reference-count":29,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Advanced Engineering Informatics"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.aei.2026.104861","type":"journal-article","created":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T09:57:50Z","timestamp":1780567070000},"page":"104861","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["RLeHLDD: A reinforcement learning enabled Human-in-the-Loop framework for multi-domain Deepfake Detection approach"],"prefix":"10.1016","volume":"76","author":[{"given":"Aparna Rajesh","family":"Atmakuri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2492-3312","authenticated-orcid":false,"given":"K. Hemant Kumar","family":"Reddy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.aei.2026.104861_b1","doi-asserted-by":"crossref","unstructured":"S.A. Khan, D.-T. Dang-Nguyen, Hybrid transformer network for deepfake detection, in: Proceedings of the 19th International Conference on Content-Based Multimedia Indexing, 2022, pp. 8\u201314.","DOI":"10.1145\/3549555.3549588"},{"key":"10.1016\/j.aei.2026.104861_b2","doi-asserted-by":"crossref","DOI":"10.3389\/fdata.2025.1569147","article-title":"Design and development of an efficient rlnet prediction model for deepfake video detection","volume":"8","author":"Bhandarkawthekar","year":"2025","journal-title":"Front. Big Data"},{"key":"10.1016\/j.aei.2026.104861_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.cviu.2025.104375","article-title":"Convolutional neural network framework for deepfake detection: A diffusion-based approach","author":"Pintelas","year":"2025","journal-title":"Comput. Vis. Image Underst."},{"key":"10.1016\/j.aei.2026.104861_b4","doi-asserted-by":"crossref","unstructured":"A. Rossler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, M. Nie\u00dfner, Faceforensics++: Learning to detect manipulated facial images, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, pp. 1\u201311.","DOI":"10.1109\/ICCV.2019.00009"},{"key":"10.1016\/j.aei.2026.104861_b5","doi-asserted-by":"crossref","unstructured":"Y. Li, X. Yang, P. Sun, H. Qi, S. Lyu, Celeb-df: A large-scale challenging dataset for deepfake forensics, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 3207\u20133216.","DOI":"10.1109\/CVPR42600.2020.00327"},{"key":"10.1016\/j.aei.2026.104861_b6","series-title":"The deepfake detection challenge (dfdc) dataset","author":"Dolhansky","year":"2020"},{"key":"10.1016\/j.aei.2026.104861_b7","doi-asserted-by":"crossref","unstructured":"A.V. Nadimpalli, A. Rattani, On improving cross-dataset generalization of deepfake detectors, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 91\u201399.","DOI":"10.1109\/CVPRW56347.2022.00019"},{"key":"10.1016\/j.aei.2026.104861_b8","series-title":"BusterX: MLLM-powered AI-generated video forgery detection and explanation","author":"Wen","year":"2025"},{"key":"10.1016\/j.aei.2026.104861_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.cose.2024.104287","article-title":"Gamifying information security: Adversarial risk exploration for IT\/OT infrastructures","volume":"151","author":"Luh","year":"2025","journal-title":"Comput. Secur."},{"key":"10.1016\/j.aei.2026.104861_b10","doi-asserted-by":"crossref","unstructured":"R. Durall, M. Keuper, J. Keuper, Watch your up-convolution: Cnn based generative deep neural networks are failing to reproduce spectral distributions, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 7890\u20137899.","DOI":"10.1109\/CVPR42600.2020.00791"},{"key":"10.1016\/j.aei.2026.104861_b11","series-title":"International Conference on Machine Learning","first-page":"3247","article-title":"Leveraging frequency analysis for deep fake image recognition","author":"Frank","year":"2020"},{"key":"10.1016\/j.aei.2026.104861_b12","series-title":"PRPO: Paragraph-level policy optimization for vision-language deepfake detection","author":"Nguyen","year":"2025"},{"key":"10.1016\/j.aei.2026.104861_b13","series-title":"2024 IEEE 9th European Symposium on Security and Privacy","first-page":"1","article-title":"GOTCHA: Real-time video deepfake detection via challenge-response","author":"Mittal","year":"2024"},{"issue":"7","key":"10.1016\/j.aei.2026.104861_b14","doi-asserted-by":"crossref","first-page":"4854","DOI":"10.1109\/TCSVT.2021.3133859","article-title":"MSTA-Net: Forgery detection by generating manipulation trace based on multi-scale self-texture attention","volume":"32","author":"Yang","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.aei.2026.104861_b15","doi-asserted-by":"crossref","first-page":"19759","DOI":"10.1007\/s00521-024-10181-7","article-title":"Deepfake detection using convolutional vision transformers and convolutional neural networks","volume":"36","author":"Soudy","year":"2024","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.aei.2026.104861_b16","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1109\/TIFS.2023.3324739","article-title":"Constructing new backbone networks via space-frequency interactive convolution for deepfake detection","volume":"19","author":"Guo","year":"2023","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.aei.2026.104861_b17","doi-asserted-by":"crossref","first-page":"3008","DOI":"10.1109\/TIFS.2022.3198275","article-title":"Hierarchical frequency-assisted interactive networks for face manipulation detection","volume":"17","author":"Miao","year":"2022","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.aei.2026.104861_b18","doi-asserted-by":"crossref","first-page":"1741","DOI":"10.1109\/TIFS.2022.3169921","article-title":"Detect and locate: Exposing face manipulation by semantic-and noise-level telltales","volume":"17","author":"Kong","year":"2022","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.aei.2026.104861_b19","first-page":"29685","article-title":"GODDS: The global online deepfake detection system","volume":"vol. 39","author":"Postiglione","year":"2025"},{"key":"10.1016\/j.aei.2026.104861_b20","series-title":"European Conference on Computer Vision","first-page":"18","article-title":"Explaining deepfake detection by analysing image matching","author":"Dong","year":"2022"},{"issue":"1","key":"10.1016\/j.aei.2026.104861_b21","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2110013119","article-title":"Deepfake detection by human crowds, machines, and machine-informed crowds","volume":"119","author":"Groh","year":"2022","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"1","key":"10.1016\/j.aei.2026.104861_b22","doi-asserted-by":"crossref","first-page":"6","DOI":"10.3390\/make8010006","article-title":"Research frontiers in machine learning & knowledge extraction","volume":"8","author":"Holzinger","year":"2025","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"10.1016\/j.aei.2026.104861_b23","series-title":"Towards robust audio deepfake detection: A evolving benchmark for continual learning","author":"Zhang","year":"2024"},{"key":"10.1016\/j.aei.2026.104861_b24","series-title":"RAIDX: A retrieval-augmented generation and GRPO reinforcement learning framework for explainable deepfake detection","author":"Li","year":"2025"},{"key":"10.1016\/j.aei.2026.104861_b25","doi-asserted-by":"crossref","first-page":"3541","DOI":"10.1109\/TIP.2022.3172845","article-title":"Deepfake forensics via an adversarial game","volume":"31","author":"Wang","year":"2022","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"10.1016\/j.aei.2026.104861_b26","first-page":"1575","article-title":"Deepfake detection using adversarial neural network","volume":"143","author":"Selvaraj","year":"2025","journal-title":"Comput. Model. Eng. Sci."},{"key":"10.1016\/j.aei.2026.104861_b27","doi-asserted-by":"crossref","unstructured":"A. Rossler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, M. Nie\u00dfner, Faceforensics++: Learning to detect manipulated facial images, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, pp. 1\u201311.","DOI":"10.1109\/ICCV.2019.00009"},{"issue":"2","key":"10.1016\/j.aei.2026.104861_b28","first-page":"98","article-title":"Multi model deep fake detection using vision transformers (ViT) and hybrid deep learning techniques","volume":"5","author":"Adinarayana","year":"2026","journal-title":"Int. J. Data Sci. IoT Manag. Syst."},{"issue":"12","key":"10.1016\/j.aei.2026.104861_b29","article-title":"TSFF-Net: A deep fake video detection model based on two-stream feature domain fusion","volume":"19","author":"Zhang","year":"2024","journal-title":"PLoS One"}],"container-title":["Advanced Engineering Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626005537?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626005537?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T12:03:30Z","timestamp":1780574610000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1474034626005537"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":29,"alternative-id":["S1474034626005537"],"URL":"https:\/\/doi.org\/10.1016\/j.aei.2026.104861","relation":{},"ISSN":["1474-0346"],"issn-type":[{"value":"1474-0346","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"RLeHLDD: A reinforcement learning enabled Human-in-the-Loop framework for multi-domain Deepfake Detection approach","name":"articletitle","label":"Article Title"},{"value":"Advanced Engineering Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aei.2026.104861","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104861"}}