{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T15:53:44Z","timestamp":1780588424373,"version":"3.54.1"},"reference-count":149,"publisher":"Elsevier BV","issue":"4","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T00:00:00Z","timestamp":1741824000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Data and Information Management"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1016\/j.dim.2025.100099","type":"journal-article","created":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T03:19:49Z","timestamp":1743477589000},"page":"100099","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":9,"title":["DeepFake video detection: Insights into model generalisation \u2014 A Systematic review"],"prefix":"10.1016","volume":"9","author":[{"given":"Ramcharan","family":"Ramanaharan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9364-0140","authenticated-orcid":false,"given":"Deepani B.","family":"Guruge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johnson I.","family":"Agbinya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.dim.2025.100099_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.124260","article-title":"Unmasking deepfakes: A systematic review of deepfake detection and generation techniques using artificial intelligence","author":"Abbas","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.dim.2025.100099_b2","series-title":"2023 7th international symposium on innovative approaches in smart technologies","first-page":"1","article-title":"Enhanced preprocessing stage for feature extraction of deepfake detection based on deep learning methods","author":"Abdulhamid","year":"2023"},{"issue":"3","key":"10.1016\/j.dim.2025.100099_b3","article-title":"DeepFake detection improvement for images based on a proposed method for local binary pattern of the multiple-channel color space","volume":"16","author":"Abdullah","year":"2023","journal-title":"International Journal of Intelligent Engineering & Systems"},{"key":"10.1016\/j.dim.2025.100099_b4","series-title":"2022 international conference on data science and intelligent computing","first-page":"65","article-title":"Deepfake video detection based on convolutional neural networks","author":"Adnan","year":"2022"},{"key":"10.1016\/j.dim.2025.100099_b5","series-title":"2021 IEEE seventh international conference on big data computing service and applications (bigDataService)","first-page":"69","article-title":"Deepfake detection using GAN discriminators","author":"Aduwala","year":"2021"},{"key":"10.1016\/j.dim.2025.100099_b6","series-title":"2018 IEEE international workshop on information forensics and security","first-page":"1","article-title":"Mesonet: a compact facial video forgery detection network","author":"Afchar","year":"2018"},{"key":"10.1016\/j.dim.2025.100099_b7","series-title":"2021 16th IEEE international conference on automatic face and gesture recognition (FG 2021)","first-page":"1","article-title":"MD-csdnetwork: Multi-domain cross stitched network for deepfake detection","author":"Agarwal","year":"2021"},{"key":"10.1016\/j.dim.2025.100099_b8","series-title":"2020 IEEE international workshop on information forensics and security","first-page":"1","article-title":"Detecting deep-fake videos from appearance and behavior","author":"Agarwal","year":"2020"},{"issue":"2","key":"10.1016\/j.dim.2025.100099_b9","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1007\/s13204-021-02072-3","article-title":"RETRACTED ARTICLE: Deepfake detection using rationale-augmented convolutional neural network","volume":"13","author":"Ahmed","year":"2023","journal-title":"Applied Nanoscience"},{"key":"10.1016\/j.dim.2025.100099_b10","series-title":"2021 international seminar on machine learning, optimization, and data science","first-page":"254","article-title":"Quick classification of xception and resnet-50 models on deepfake video using local binary pattern","author":"Arini","year":"2022"},{"issue":"5","key":"10.1016\/j.dim.2025.100099_b11","doi-asserted-by":"crossref","first-page":"1371","DOI":"10.1007\/s10207-023-00695-x","article-title":"A defensive framework for deepfake detection under adversarial settings using temporal and spatial features","volume":"22","author":"Asha","year":"2023","journal-title":"International Journal of Information Security"},{"issue":"5","key":"10.1016\/j.dim.2025.100099_b12","doi-asserted-by":"crossref","first-page":"3095","DOI":"10.3390\/app13053095","article-title":"Real-time advanced computational intelligence for deep fake video detection","volume":"13","author":"Bansal","year":"2023","journal-title":"Applied Sciences"},{"key":"10.1016\/j.dim.2025.100099_b13","series-title":"2023 second international conference on informatics","first-page":"1","article-title":"Artifact based deepfake detection methods","author":"Bansal","year":"2023"},{"key":"10.1016\/j.dim.2025.100099_b14","doi-asserted-by":"crossref","unstructured":"Baxevana-kis, S., Kordopatis-Zilos, G., Galopoulos, P., Apostolidis, L., Levacher, K., Baris Schlicht, I., et al. (2022). The mever deepfake detection service: Lessons learnt from developing and deploying in the wild. In Proceedings of the 1st international workshop on multimedia AI against disinformation (pp. 59\u201368).","DOI":"10.1145\/3512732.3533587"},{"key":"10.1016\/j.dim.2025.100099_b15","doi-asserted-by":"crossref","unstructured":"Beuve, N., Hamidouche, W., & Deforges, O. (2021). DmyT: Dummy triplet loss for deepfake detection. In Proceedings of the 1st workshop on synthetic multimedia-audiovisual deepfake generation and detection (pp. 17\u201324).","DOI":"10.1145\/3476099.3484316"},{"issue":"3","key":"10.1016\/j.dim.2025.100099_b16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3626101","article-title":"Hierarchical learning and dummy triplet loss for efficient deepfake detection","volume":"20","author":"Beuve","year":"2023","journal-title":"ACM Transactions on Multimedia Computing, Communications and Applications"},{"key":"10.1016\/j.dim.2025.100099_b17","doi-asserted-by":"crossref","unstructured":"Bhaumik, K. K., & Woo, S. S. (2023). Exploiting Inconsistencies in Object Representations for Deepfake Video Detection. In Proceedings of the 2nd workshop on security implications of deepfakes and cheapfakes (pp. 11\u201315).","DOI":"10.1145\/3595353.3595885"},{"key":"10.1016\/j.dim.2025.100099_b18","series-title":"2023 3rd international conference on intelligent technologies","first-page":"1","article-title":"Implementation of a deepfake detection system using convolutional neural networks and adversarial training","author":"Bomma-reddy","year":"2023"},{"key":"10.1016\/j.dim.2025.100099_b19","series-title":"2020 IEEE international workshop on information forensics and security","first-page":"1","article-title":"Training strategies and data augmentations in cnn-based deepfake video detection","author":"Bondi","year":"2020"},{"key":"10.1016\/j.dim.2025.100099_b20","series-title":"2020 IEEE applied imagery pattern recognition workshop","first-page":"1","article-title":"Deepfakes detection in videos using feature engineering techniques in deep learning convolution neural network frameworks","author":"Burroughs","year":"2020"},{"key":"10.1016\/j.dim.2025.100099_b21","series-title":"2020 39th Chinese control conference","first-page":"7252","article-title":"Deepfake face image detection based on improved VGG convolutional neural network","author":"Chang","year":"2020"},{"key":"10.1016\/j.dim.2025.100099_b22","series-title":"Odyssey","first-page":"132","article-title":"Generalization of audio deepfake detection","author":"Chen","year":"2020"},{"issue":"3","key":"10.1016\/j.dim.2025.100099_b23","doi-asserted-by":"crossref","first-page":"1468","DOI":"10.1109\/TCSVT.2022.3209336","article-title":"Learning features of intra-consistency and inter-diversity: Keys toward generalizable deepfake detection","volume":"33","author":"Chen","year":"2022","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"10.1016\/j.dim.2025.100099_b24","first-page":"1","article-title":"Featuretransfer: Unsupervised domain adaptation for cross-domain deepfake detection","volume":"2021","author":"Chen","year":"2021","journal-title":"Security and Communication Networks"},{"issue":"5","key":"10.1016\/j.dim.2025.100099_b25","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1109\/JSTSP.2020.2999185","article-title":"Recurrent convolutional structures for audio spoof and video deepfake detection","volume":"14","author":"Chintha","year":"2020","journal-title":"IEEE Journal of Selected Topics in Signal Processing"},{"key":"10.1016\/j.dim.2025.100099_b26","unstructured":"Cho, B., Le, B. M., Kim, J., Woo, S., Tariq, S., Abuadbba, A., et al. (2023). Towards understanding of deepfake videos in the wild. In Proceedings of the 32nd ACM international conference on information and knowledge management (pp. 4530\u20134537)."},{"key":"10.1016\/j.dim.2025.100099_b27","doi-asserted-by":"crossref","unstructured":"Choi, J., Kim, T., Jeong, Y., Baek, S., & Choi, J. (2024). Exploiting Style Latent Flows for Generalizing Deepfake Video Detection. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 1133\u20131143).","DOI":"10.1109\/CVPR52733.2024.00114"},{"issue":"19","key":"10.1016\/j.dim.2025.100099_b28","doi-asserted-by":"crossref","first-page":"21799","DOI":"10.1007\/s10489-023-04620-6","article-title":"Capsule networks for computer vision applications: a comprehensive review","volume":"53","author":"Choudhary","year":"2023","journal-title":"Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies"},{"key":"10.1016\/j.dim.2025.100099_b29","doi-asserted-by":"crossref","unstructured":"Chugh, K., Gupta, P., Dhall, A., & Subramanian, R. (2020). Not made for each other-audio-visual dissonance-based deepfake detection and localization. In Proceedings of the 28th ACM international conference on multimedia (pp. 439\u2013447).","DOI":"10.1145\/3394171.3413700"},{"issue":"5","key":"10.1016\/j.dim.2025.100099_b30","doi-asserted-by":"crossref","first-page":"89","DOI":"10.3390\/jimaging9050089","article-title":"On the generalization of deep learning models in video deepfake detection","volume":"9","author":"Coccomini","year":"2023","journal-title":"Journal of Imaging"},{"key":"10.1016\/j.dim.2025.100099_b31","doi-asserted-by":"crossref","unstructured":"Coccomini, D. A., Caldelli, R., Falchi, F., Gennaro, C., & Amato, G. (2022). Cross-forgery analysis of vision transformers and cnns for deepfake image detection. In Proceedings of the 1st international workshop on multimedia AI against disinformation (pp. 52\u201358).","DOI":"10.1145\/3512732.3533582"},{"key":"10.1016\/j.dim.2025.100099_b32","doi-asserted-by":"crossref","DOI":"10.1155\/2022\/3441549","article-title":"Deepfake video detection based on EfficientNet-V2 network","volume":"2022","author":"Deng","year":"2022","journal-title":"Computational Intelligence and Neuroscience"},{"key":"10.1016\/j.dim.2025.100099_b33","doi-asserted-by":"crossref","first-page":"3429","DOI":"10.1109\/TMM.2021.3098422","article-title":"Anti-forensics for face swapping videos via adversarial training","volume":"24","author":"Ding","year":"2021","journal-title":"IEEE Transactions on Multimedia"},{"key":"10.1016\/j.dim.2025.100099_b34","series-title":"European conference on computer vision","first-page":"18","article-title":"Explaining deepfake detection by analysing image matching","author":"Dong","year":"2022"},{"key":"10.1016\/j.dim.2025.100099_b35","series-title":"Proceedings of the 29th ACM international conference on information & knowledge management","first-page":"325","article-title":"Towards generalizable deepfake detection with locality-aware AutoEncoder","author":"Du","year":"2020"},{"key":"10.1016\/j.dim.2025.100099_b36","series-title":"Proceedings of the 14th ACM web science conference 2022","first-page":"154","article-title":"On pushing DeepFake tweet detection capabilities to the limits","author":"Gambini","year":"2022"},{"key":"10.1016\/j.dim.2025.100099_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.118423","article-title":"ViXNet: Vision transformer with xception network for deepfakes based video and image forgery detection","volume":"210","author":"Ganguly","year":"2022","journal-title":"Expert Systems with Applications"},{"issue":"4","key":"10.1016\/j.dim.2025.100099_b38","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1007\/s10044-022-01083-2","article-title":"Visual attention-based deepfake video forgery detection","volume":"25","author":"Ganguly","year":"2022","journal-title":"Pattern Analysis and Applications"},{"key":"10.1016\/j.dim.2025.100099_b39","series-title":"Spatio-temporal features for generalized detection of deepfake videos","author":"Gani-yusufoglu","year":"2020"},{"key":"10.1016\/j.dim.2025.100099_b40","series-title":"2022 IEEE 1st international conference on data, decision and systems","first-page":"1","article-title":"AI based deepfake detection","author":"Garde","year":"2022"},{"key":"10.1016\/j.dim.2025.100099_b41","doi-asserted-by":"crossref","unstructured":"Ge, S., Lin, F., Li, C., Zhang, D., Tan, J., Wang, W., et al. (2021). Latent pattern sensing: Deepfake video detection via predictive representation learning. In Proceedings of the 3rd ACM international conference on multimedia in Asia (pp. 1\u20137).","DOI":"10.1145\/3469877.3490586"},{"issue":"2s","key":"10.1016\/j.dim.2025.100099_b42","doi-asserted-by":"crossref","DOI":"10.1145\/3536426","article-title":"Deepfake video detection via predictive representation learning","volume":"18","author":"Ge","year":"2022","journal-title":"ACM Trans. Multimedia Comput. Commun. Appl."},{"issue":"8","key":"10.1016\/j.dim.2025.100099_b43","doi-asserted-by":"crossref","first-page":"128","DOI":"10.3390\/jimaging7080128","article-title":"Fighting deepfakes by detecting gan dct anomalies","volume":"7","author":"Giudice","year":"2021","journal-title":"Journal of Imaging"},{"issue":"3","key":"10.1016\/j.dim.2025.100099_b44","article-title":"Deepfake forensics, an ai-synthesized detection with deep convolutional generative adversarial networks","volume":"9","author":"Gong","year":"2020","journal-title":"International Journal"},{"issue":"6","key":"10.1016\/j.dim.2025.100099_b45","doi-asserted-by":"crossref","first-page":"201","DOI":"10.14569\/IJACSA.2021.0120622","article-title":"DeepfakeNet, an efficient deepfake detection method","volume":"12","author":"Gong","year":"2021","journal-title":"International Journal of Advanced Computer Science and Applications"},{"key":"10.1016\/j.dim.2025.100099_b46","doi-asserted-by":"crossref","unstructured":"Gu, Z., Chen, Y., Yao, T., Ding, S., Li, J., Huang, F., et al. (2021). Spatiotemporal inconsistency learning for deepfake video detection. In Proceedings of the 29th ACM international conference on multimedia (pp. 3473\u20133481).","DOI":"10.1145\/3474085.3475508"},{"issue":"10","key":"10.1016\/j.dim.2025.100099_b47","doi-asserted-by":"crossref","first-page":"263","DOI":"10.3390\/jimaging8100263","article-title":"The face deepfake detection challenge","volume":"8","author":"Guarnera","year":"2022","journal-title":"Journal of Imaging"},{"key":"10.1016\/j.dim.2025.100099_b48","series-title":"2023 international conference on smart computing and application","first-page":"1","article-title":"Deep learning based DeepFake video detection","author":"Guefrachi","year":"2023"},{"key":"10.1016\/j.dim.2025.100099_b49","series-title":"2022 6th international conference on advanced technologies for signal and image processing","first-page":"1","article-title":"DeepFake video detection using InceptionResnetV2","author":"Guefrechi","year":"2022"},{"issue":"2","key":"10.1016\/j.dim.2025.100099_b50","article-title":"Deepfake detection using deep learning methods: A systematic and comprehensive review","volume":"14","author":"Heidari","year":"2024","journal-title":"Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"},{"issue":"7","key":"10.1016\/j.dim.2025.100099_b51","doi-asserted-by":"crossref","first-page":"7512","DOI":"10.1007\/s10489-022-03867-9","article-title":"Deepfake detection algorithm based on improved vision transformer","volume":"53","author":"Heo","year":"2023","journal-title":"Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies"},{"key":"10.1016\/j.dim.2025.100099_b52","doi-asserted-by":"crossref","unstructured":"Hongmeng, Z., Zhiqiang, Z., Lei, S., Xiuqing, M., & Yuehan, W. (2020). A detection method for deepfake hard compressed videos based on super-resolution reconstruction using CNN. In Proceedings of the 2020 4th high performance computing and cluster technologies conference & 2020 3rd international conference on big data and artificial intelligence (pp. 98\u2013103).","DOI":"10.1145\/3409501.3409542"},{"key":"10.1016\/j.dim.2025.100099_b53","series-title":"2021 IEEE international conference on image processing","first-page":"3577","article-title":"Improving the generalization ability of deepfake detection via disentangled representation learning","author":"Hu","year":"2021"},{"key":"10.1016\/j.dim.2025.100099_b54","series-title":"2022 5th international conference on signal processing and information security","first-page":"11","article-title":"Detection of compressed DeepFake video drawbacks and technical developments","author":"Humidan","year":"2022"},{"key":"10.1016\/j.dim.2025.100099_b55","series-title":"2022 international conference on data analytics for business and industry","first-page":"598","article-title":"An improved deepfake detection approach with nasNetLarge CNN","author":"\u0130lhan","year":"2022"},{"issue":"16","key":"10.1016\/j.dim.2025.100099_b56","doi-asserted-by":"crossref","first-page":"5413","DOI":"10.3390\/s21165413","article-title":"A new deep learning-based methodology for video deepfake detection using xgboost","volume":"21","author":"Ismail","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.dim.2025.100099_b57","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.730","article-title":"Deepfake video detection: YOLO-face convolution recurrent approach","volume":"7","author":"Ismail","year":"2021","journal-title":"PeerJ Computer Science"},{"issue":"2","key":"10.1016\/j.dim.2025.100099_b58","doi-asserted-by":"crossref","DOI":"10.1016\/j.dim.2023.100063","article-title":"The impact of artificial intelligence on organisational cyber security: An outcome of a systematic literature review","volume":"8","author":"Jada","year":"2024","journal-title":"Data and Information Management"},{"key":"10.1016\/j.dim.2025.100099_b59","series-title":"2021 IEEE 23rd international workshop on multimedia signal processing","first-page":"1","article-title":"Improving generalization of deepfake detection by training for attribution","author":"Jain","year":"2021"},{"key":"10.1016\/j.dim.2025.100099_b60","series-title":"2022 4th international conference on current research in engineering and science applications","first-page":"228","article-title":"Facial action unit-based deepfake video detection using deep learning","author":"Jaleel","year":"2022"},{"key":"10.1016\/j.dim.2025.100099_b61","doi-asserted-by":"crossref","first-page":"1294","DOI":"10.1109\/TMM.2022.3141267","article-title":"Learning disentangled representation implicitly via transformer for occluded person re-identification","volume":"25","author":"Jia","year":"2022","journal-title":"IEEE Transactions on Multimedia"},{"issue":"4","key":"10.1016\/j.dim.2025.100099_b62","doi-asserted-by":"crossref","first-page":"403","DOI":"10.3233\/JCS-200124","article-title":"FakeFilter: A cross-distribution deepfake detection system with domain adaptation","volume":"29","author":"Jiang","year":"2021","journal-title":"Journal of Computer Security"},{"key":"10.1016\/j.dim.2025.100099_b63","series-title":"2021 IST-africa conference (IST-africa)","first-page":"1","article-title":"Deepfake detection using a two-stream capsule network","author":"Joseph","year":"2021"},{"key":"10.1016\/j.dim.2025.100099_b64","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":"10.1016\/j.dim.2025.100099_b65","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":"10.1016\/j.dim.2025.100099_b66","series-title":"2021 international conference on visual communications and image processing","first-page":"1","article-title":"Hcit: Deepfake video detection using a hybrid model of CNN features and vision transformer","author":"Kaddar","year":"2021"},{"issue":"23","key":"10.1016\/j.dim.2025.100099_b67","doi-asserted-by":"crossref","first-page":"36179","DOI":"10.1007\/s11042-023-15072-y","article-title":"Triangle and orthogonal local binary pattern for face recognition","volume":"82","author":"Karanwal","year":"2023","journal-title":"Multimedia Tools and Applications"},{"issue":"6","key":"10.1016\/j.dim.2025.100099_b68","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10462-024-10810-6","article-title":"Deepfake video detection: challenges and opportunities","volume":"57","author":"Kaur","year":"2024","journal-title":"Artificial Intelligence Review"},{"key":"10.1016\/j.dim.2025.100099_b69","series-title":"2021 international mobile, intelligent, and ubiquitous computing conference","first-page":"1","article-title":"Deepfakes creation and detection using deep learning","author":"Khalil","year":"2021"},{"issue":"4","key":"10.1016\/j.dim.2025.100099_b70","doi-asserted-by":"crossref","first-page":"93","DOI":"10.3390\/fi13040093","article-title":"Icaps-dfake: An integrated capsule-based model for deepfake image and video detection","volume":"13","author":"Khalil","year":"2021","journal-title":"Future Internet"},{"key":"10.1016\/j.dim.2025.100099_b71","doi-asserted-by":"crossref","unstructured":"Khan, S. A., & Dai, H. (2021). Video transformer for deepfake detection with incremental learning. In Proceedings of the 29th ACM international conference on multimedia (pp. 1821\u20131828).","DOI":"10.1145\/3474085.3475332"},{"key":"10.1016\/j.dim.2025.100099_b72","series-title":"Proceedings of the 19th international conference on content-based multimedia indexing","first-page":"8","article-title":"Hybrid transformer network for deepfake detection","author":"Khan","year":"2022"},{"issue":"4","key":"10.1016\/j.dim.2025.100099_b73","doi-asserted-by":"crossref","first-page":"49","DOI":"10.3390\/bdcc5040049","article-title":"Add: Attention-based deepfake detection approach","volume":"5","author":"Khormali","year":"2021","journal-title":"Big Data and Cognitive Computing"},{"issue":"6","key":"10.1016\/j.dim.2025.100099_b74","doi-asserted-by":"crossref","first-page":"2953","DOI":"10.3390\/app12062953","article-title":"DFDT: an end-to-end deepfake detection framework using vision transformer","volume":"12","author":"Khormali","year":"2022","journal-title":"Applied Sciences"},{"issue":"1","key":"10.1016\/j.dim.2025.100099_b75","first-page":"2","article-title":"Deepfake tweets detection using deep learning algorithms","volume":"20","author":"Kirn","year":"2022","journal-title":"Engineering Proceedings"},{"issue":"2","key":"10.1016\/j.dim.2025.100099_b76","doi-asserted-by":"crossref","first-page":"31","DOI":"10.3390\/systems10020031","article-title":"Multi-feature fusion based deepfake face forgery video detection","volume":"10","author":"Lai","year":"2022","journal-title":"Systems"},{"issue":"18","key":"10.1016\/j.dim.2025.100099_b77","doi-asserted-by":"crossref","first-page":"3952","DOI":"10.3390\/math11183952","article-title":"ClueCatcher: Catching domain-wise independent clues for deepfake detection","volume":"11","author":"Lee","year":"2023","journal-title":"Mathematics"},{"key":"10.1016\/j.dim.2025.100099_b78","series-title":"2020 IEEE applied imagery pattern recognition workshop","first-page":"1","article-title":"Deepfake video detection based on spatial, spectral, and temporal inconsistencies using multimodal deep learning","author":"Lewis","year":"2020"},{"key":"10.1016\/j.dim.2025.100099_b79","series-title":"Proceedings of the 28th ACM international conference on multimedia","first-page":"1864","article-title":"Sharp multiple instance learning for DeepFake video detection","author":"Li","year":"2020"},{"key":"10.1016\/j.dim.2025.100099_b80","doi-asserted-by":"crossref","unstructured":"Li, M., Li, X., Yu, K., Deng, C., Huang, H., Mao, F., et al. (2023). Spatio-temporal catcher: A self-supervised transformer for deepfake video detection. In Proceedings of the 31st ACM international conference on multimedia (pp. 8707\u20138718).","DOI":"10.1145\/3581783.3613842"},{"key":"10.1016\/j.dim.2025.100099_b81","article-title":"Detecting deepfake videos using spatiotemporal trident network","author":"Lin","year":"2023","journal-title":"ACM Trans. Multimedia Comput. Commun. Appl."},{"issue":"3","key":"10.1016\/j.dim.2025.100099_b82","doi-asserted-by":"crossref","DOI":"10.1016\/j.dim.2023.100035","article-title":"Improved detection of transient events in wide area sky survey using convolutional neural networks","volume":"8","author":"Liu","year":"2024","journal-title":"Data and Information Management"},{"key":"10.1016\/j.dim.2025.100099_b83","article-title":"FedForgery: generalized face forgery detection with residual federated learning","author":"Liu","year":"2023","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"10.1016\/j.dim.2025.100099_b84","doi-asserted-by":"crossref","unstructured":"Liu, C., Li, J., Duan, J., & Huang, H. (2022). Video Forgery Detection Using Spatio-Temporal Dual Transformer. In Proceedings of the 2022 11th international conference on computing and pattern recognition (pp. 273\u2013281).","DOI":"10.1145\/3581807.3581847"},{"issue":"9","key":"10.1016\/j.dim.2025.100099_b85","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":"International Journal of Intelligent Systems"},{"key":"10.1016\/j.dim.2025.100099_b86","series-title":"2020 IEEE applied imagery pattern recognition workshop","first-page":"1","article-title":"Multimodal approach for deepfake detection","author":"Lomnitz","year":"2020"},{"key":"10.1016\/j.dim.2025.100099_b87","doi-asserted-by":"crossref","unstructured":"Lugstein, F., Baier, S., Bachinger, G., & Uhl, A. (2021). PRNU-based deepfake detection. In Proceedings of the 2021 ACM workshop on information hiding and multimedia security (pp. 7\u201312).","DOI":"10.1145\/3437880.3460400"},{"key":"10.1016\/j.dim.2025.100099_b88","series-title":"2020 IEEE conference of Russian Young researchers in electrical and electronic engineering (eIConRus)","first-page":"408","article-title":"Methods of deepfake detection based on machine learning","author":"Maksutov","year":"2020"},{"key":"10.1016\/j.dim.2025.100099_b89","doi-asserted-by":"crossref","first-page":"18757","DOI":"10.1109\/ACCESS.2022.3151186","article-title":"DeepFake detection for human face images and videos: A survey","volume":"10","author":"Malik","year":"2022","journal-title":"Ieee Access"},{"key":"10.1016\/j.dim.2025.100099_b90","series-title":"2023 3rd international conference on electrical, computer, communications and mechatronics engineering","first-page":"1","article-title":"Hybrid deepfake detection utilizing MLP and LSTM","author":"Mallet","year":"2023"},{"issue":"4","key":"10.1016\/j.dim.2025.100099_b91","doi-asserted-by":"crossref","first-page":"3974","DOI":"10.1007\/s10489-022-03766-z","article-title":"Deepfakes generation and detection: State-of-the-art, open challenges, countermeasures, and way forward","volume":"53","author":"Masood","year":"2023","journal-title":"Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies"},{"issue":"8","key":"10.1016\/j.dim.2025.100099_b92","doi-asserted-by":"crossref","first-page":"4029","DOI":"10.1007\/s11760-023-02633-9","article-title":"LW-DeepFakeNet: a lightweight time distributed CNN-LSTM network for real-time DeepFake video detection","volume":"17","author":"Masud","year":"2023","journal-title":"Signal, Image and Video Processing"},{"key":"10.1016\/j.dim.2025.100099_b93","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.procs.2023.01.283","article-title":"The effect of deep learning methods on deepfake audio detection for digital investigation","volume":"219","author":"Mcuba","year":"2023","journal-title":"Procedia Computer Science"},{"key":"10.1016\/j.dim.2025.100099_b94","doi-asserted-by":"crossref","unstructured":"Mehta, V., Gupta, P., Subramanian, R., & Dhall, A. (2021). Fakebuster: a deepfakes detection tool for video conferencing scenarios. In Companion proceedings of the 26th international conference on intelligent user interfaces (pp. 61\u201363).","DOI":"10.1145\/3397482.3450726"},{"key":"10.1016\/j.dim.2025.100099_b95","series-title":"2023 IEEE IAS global conference on emerging technologies (globConET)","first-page":"1","article-title":"Deep learning technique for recognition of deep fake videos","author":"Mira","year":"2023"},{"issue":"1","key":"10.1016\/j.dim.2025.100099_b96","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 Computing Surveys"},{"key":"10.1016\/j.dim.2025.100099_b97","series-title":"From deepfake to deep useful: risks and opportunities through a systematic literature review","author":"Misirlis","year":"2023"},{"key":"10.1016\/j.dim.2025.100099_b98","series-title":"2020 IEEE international symposium on smart electronic systems (ISES)(formerly iNiS)","first-page":"91","article-title":"A novel machine learning based method for deepfake video detection in social media","author":"Mitra","year":"2020"},{"issue":"2","key":"10.1016\/j.dim.2025.100099_b99","doi-asserted-by":"crossref","first-page":"98","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 Computer Science"},{"issue":"2","key":"10.1016\/j.dim.2025.100099_b100","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1016\/j.jksuci.2023.01.014","article-title":"A comprehensive review on ensemble deep learning: Opportunities and challenges","volume":"35","author":"Mohammed","year":"2023","journal-title":"Journal of King Saud University-Computer and Information Sciences"},{"key":"10.1016\/j.dim.2025.100099_b101","series-title":"Integrating audio-visual features for multimodal deepfake detection","author":"Muppalla","year":"2023"},{"issue":"1","key":"10.1016\/j.dim.2025.100099_b102","doi-asserted-by":"crossref","first-page":"1","DOI":"10.36548\/jucct.2022.1.001","article-title":"Extensive analysis of deep learning-based deepfake video detection","volume":"4","author":"Myvizhi","year":"2022","journal-title":"Journal of Ubiquitous Computing and Communication Technologies"},{"key":"10.1016\/j.dim.2025.100099_b103","article-title":"ProActive DeepFake detection using GAN-based visible watermarking","author":"Nadimpalli","year":"2023","journal-title":"ACM Trans. Multimedia Comput. Commun. Appl."},{"key":"10.1016\/j.dim.2025.100099_b104","article-title":"Learning spatio-temporal features to detect manipulated facial videos created by the deepfake techniques","volume":"36","author":"Nguyen","year":"2021","journal-title":"Forensic Science International: Digital Investigation"},{"issue":"10","key":"10.1016\/j.dim.2025.100099_b105","doi-asserted-by":"crossref","first-page":"6111","DOI":"10.1109\/TPAMI.2021.3093446","article-title":"Deepfake detection based on discrepancies between faces and their context","volume":"44","author":"Nirkin","year":"2021","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.dim.2025.100099_b106","doi-asserted-by":"crossref","first-page":"n160","DOI":"10.1136\/bmj.n160","article-title":"PRISMA 2020 explanation and elaboration: Updated guidance and exemplars for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"issue":"8","key":"10.1016\/j.dim.2025.100099_b107","doi-asserted-by":"crossref","DOI":"10.1111\/exsy.13570","article-title":"A review of deep learning-based approaches for deepfake content detection","volume":"41","author":"Passos","year":"2024","journal-title":"Expert Systems"},{"key":"10.1016\/j.dim.2025.100099_b108","series-title":"2023 14th international conference on computing communication and networking technologies","first-page":"1","article-title":"Deepfake detection using custom densenet","author":"Pasupuleti","year":"2023"},{"key":"10.1016\/j.dim.2025.100099_b109","series-title":"Deepfake detection analyzing hybrid dataset utilizing CNN and SVM","author":"Pryor","year":"2023"},{"key":"10.1016\/j.dim.2025.100099_b110","series-title":"2022 IEEE 10th region 10 humanitarian technology conference (r10-HTC)","first-page":"259","article-title":"Short and low resolution deepfake video detection using cnn","author":"Rahman","year":"2022"},{"key":"10.1016\/j.dim.2025.100099_b111","series-title":"2021 international carnahan conference on security technology","first-page":"1","article-title":"An experimental evaluation on deepfake detection using deep face recognition","author":"Rama chandran","year":"2021"},{"key":"10.1016\/j.dim.2025.100099_b112","doi-asserted-by":"crossref","first-page":"25494","DOI":"10.1109\/ACCESS.2022.3154404","article-title":"Deepfake detection: A systematic literature review","volume":"10","author":"Rana","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.dim.2025.100099_b113","series-title":"2020 7th IEEE international conference on cyber security and cloud computing (cSCloud)\/2020 6th IEEE international conference on edge computing and scalable cloud (edgeCom)","first-page":"70","article-title":"Deepfakestack: A deep ensemble-based learning technique for deepfake detection","author":"Rana","year":"2020"},{"key":"10.1016\/j.dim.2025.100099_b114","series-title":"2020 3rd international conference on information and computer technologies","first-page":"86","article-title":"Improved generalizability of deep-fakes detection using transfer learning based CNN framework","author":"Ranjan","year":"2020"},{"key":"10.1016\/j.dim.2025.100099_b115","series-title":"2022 international joint conference on neural networks","first-page":"1","article-title":"A hybrid CNN-LSTM model for video deepfake detection by leveraging optical flow features","author":"Saikia","year":"2022"},{"key":"10.1016\/j.dim.2025.100099_b116","series-title":"Local binary pattern (LBP) optimization for feature extraction","author":"Sedaghatjoo","year":"2024"},{"key":"10.1016\/j.dim.2025.100099_b117","first-page":"1","article-title":"A systematic literature review on deepfake detection techniques","author":"Sharma","year":"2024","journal-title":"Multimedia Tools and Applications"},{"issue":"7","key":"10.1016\/j.dim.2025.100099_b118","doi-asserted-by":"crossref","first-page":"108","DOI":"10.3390\/jimaging7070108","article-title":"Media forensics considerations on deepfake detection with hand-crafted features","volume":"7","author":"Siegel","year":"2021","journal-title":"Journal of Imaging"},{"key":"10.1016\/j.dim.2025.100099_b119","series-title":"DeepFake video detection: a time-distributed approach. SN comput sci 1: 212","author":"Singh","year":"2020"},{"key":"10.1016\/j.dim.2025.100099_b120","doi-asserted-by":"crossref","unstructured":"Stanciu, D. C., & Ionescu, B. (2022). Uncovering the Strength of Capsule Networks in Deepfake Detection. In Proceedings of the 1st international workshop on multimedia AI against disinformation (pp. 69\u201377).","DOI":"10.1145\/3512732.3533581"},{"key":"10.1016\/j.dim.2025.100099_b121","doi-asserted-by":"crossref","unstructured":"Stanciu, D.-C., & Ionescu, B. (2023). Autoencoder-based data augmentation for deepfake detection. In Proceedings of the 2nd ACM International Workshop on Multimedia AI against Disinformation (pp. 19\u201327).","DOI":"10.1145\/3592572.3592840"},{"key":"10.1016\/j.dim.2025.100099_b122","series-title":"2022 5th international conference of computer and informatics engineering (IC2IE)","first-page":"22","article-title":"Usage of convolutional neural network for deepfake video detection with face-swapping technique","author":"Stephen","year":"2022"},{"issue":"2","key":"10.1016\/j.dim.2025.100099_b123","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1080\/23742917.2023.2192888","article-title":"A systematic literature review on the effectiveness of deepfake detection techniques","volume":"7","author":"Stroebel","year":"2023","journal-title":"Journal of Cyber Security Technology"},{"key":"10.1016\/j.dim.2025.100099_b124","series-title":"2020 11th international conference on computing, communication and networking technologies","first-page":"1","article-title":"Employing transfer-learning based CNN architectures to enhance the generalizability of deepfake detection","author":"Suratkar","year":"2020"},{"key":"10.1016\/j.dim.2025.100099_b125","series-title":"2020 IEEE 17th India council international conference","first-page":"1","article-title":"Exposing deepfakes using convolutional neural networks and transfer learning approaches","author":"Suratkar","year":"2020"},{"issue":"1","key":"10.1016\/j.dim.2025.100099_b126","doi-asserted-by":"crossref","first-page":"89","DOI":"10.3390\/jcp2010007","article-title":"Comparison of deepfake detection techniques through deep learning","volume":"2","author":"Taeb","year":"2022","journal-title":"Journal of Cybersecurity and Privacy"},{"issue":"1","key":"10.1016\/j.dim.2025.100099_b127","doi-asserted-by":"crossref","DOI":"10.1145\/3629976","article-title":"DeepMark: A scalable and robust framework for DeepFake video detection","volume":"27","author":"Tang","year":"2024","journal-title":"ACM Trans. Priv. Secur."},{"key":"10.1016\/j.dim.2025.100099_b128","series-title":"A convolutional lstm based residual network for deepfake video detection","author":"Tariq","year":"2020"},{"issue":"1","key":"10.1016\/j.dim.2025.100099_b129","first-page":"14","article-title":"Deepfakes: An integrative review of the literature and an agenda for future research","volume":"51","author":"Vasist","year":"2022","journal-title":"Communications of the Association for Information Systems"},{"key":"10.1016\/j.dim.2025.100099_b130","article-title":"Attention is all you need","author":"Vaswani","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"4","key":"10.1016\/j.dim.2025.100099_b131","doi-asserted-by":"crossref","first-page":"503","DOI":"10.31803\/tg-20220403080215","article-title":"Afmb-net: Deepfake detection network using heart rate analysis","volume":"16","author":"Vinay","year":"2022","journal-title":"Tehni\u010cki Glasnik"},{"issue":"6","key":"10.1016\/j.dim.2025.100099_b132","first-page":"1","article-title":"Deep convolutional pooling transformer for deepfake detection","volume":"19","author":"Wang","year":"2023","journal-title":"ACM Transactions on Multimedia Computing, Communications and Applications"},{"key":"10.1016\/j.dim.2025.100099_b133","series-title":"2020 15Th IEEE international conference on automatic face and gesture recognition (FG 2020)","first-page":"515","article-title":"A video is worth more than 1000 lies. Comparing 3DCNN approaches for detecting deepfakes","author":"Wang","year":"2020"},{"key":"10.1016\/j.dim.2025.100099_b134","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1016\/j.ins.2022.03.026","article-title":"FFR_FD: Effective and fast detection of DeepFakes via feature point defects","volume":"596","author":"Wang","year":"2022","journal-title":"Information Sciences"},{"key":"10.1016\/j.dim.2025.100099_b135","doi-asserted-by":"crossref","unstructured":"Wang, J., Wu, Z., Ouyang, W., Han, X., Chen, J., Jiang, Y.-G., et al. (2022). M2tr: Multi-modal multi-scale transformers for deepfake detection. In Proceedings of the 2022 international conference on multimedia retrieval (pp. 615\u2013623).","DOI":"10.1145\/3512527.3531415"},{"key":"10.1016\/j.dim.2025.100099_b136","doi-asserted-by":"crossref","DOI":"10.1016\/j.technovation.2023.102784","article-title":"Mapping the deepfake landscape for innovation: A multidisciplinary systematic review and future research agenda","volume":"125","author":"Whittaker","year":"2023","journal-title":"Technovation"},{"key":"10.1016\/j.dim.2025.100099_b137","article-title":"Forgery detection by weighted complementarity between significant invariance and detail enhancement","author":"Xiao","year":"2023","journal-title":"ACM Trans. Multimedia Comput. Commun. Appl."},{"key":"10.1016\/j.dim.2025.100099_b138","series-title":"2022 13th international conference on computing communication and networking technologies","first-page":"1","article-title":"Robust and generalized DeepFake detection","author":"Yadav","year":"2022"},{"key":"10.1016\/j.dim.2025.100099_b139","doi-asserted-by":"crossref","unstructured":"Yang, T., Chen, K., & Zhong, S. (2023). Deepfake Detection Using Fusion Channel Information in a Multi-attentional Model. In Proceedings of the 2023 Asia conference on artificial intelligence, machine learning and robotics (pp. 1\u20135).","DOI":"10.1145\/3625343.3625365"},{"key":"10.1016\/j.dim.2025.100099_b140","series-title":"2020 international conference on computer science and software engineering","first-page":"186","article-title":"Effective and fast deepfake detection method based on haar wavelet transform","author":"Younus","year":"2020"},{"key":"10.1016\/j.dim.2025.100099_b141","doi-asserted-by":"crossref","unstructured":"Zhang, D., Li, C., Lin, F., Zeng, D., & Ge, S. (2021). Detecting Deepfake Videos with Temporal Dropout 3DCNN.. In IJCAI (pp. 1288\u20131294).","DOI":"10.24963\/ijcai.2021\/178"},{"key":"10.1016\/j.dim.2025.100099_b142","series-title":"Proceedings of the 30th ACM international conference on multimedia","first-page":"5833","article-title":"Deepfake video detection with spatiotemporal dropout transformer","author":"Zhang","year":"2022"},{"issue":"5","key":"10.1016\/j.dim.2025.100099_b143","doi-asserted-by":"crossref","DOI":"10.1145\/3625100","article-title":"Joint audio-visual attention with contrastive learning for more general deepfake detection","volume":"20","author":"Zhang","year":"2024","journal-title":"ACM Trans. Multimedia Comput. Commun. Appl."},{"key":"10.1016\/j.dim.2025.100099_b144","series-title":"Proceedings of the 1st international workshop on deepfake detection for audio multimedia","first-page":"43","article-title":"Deepfake detection system for the ADD challenge track 3.2 based on score fusion","author":"Zhang","year":"2022"},{"issue":"5","key":"10.1016\/j.dim.2025.100099_b145","first-page":"1671","article-title":"Cascaded-hop for deepfake videos detection","volume":"16","author":"Zhang","year":"2022","journal-title":"KSII Transactions on Internet and Information Systems (TIIS)"},{"issue":"2","key":"10.1016\/j.dim.2025.100099_b146","doi-asserted-by":"crossref","first-page":"249","DOI":"10.3390\/e22020249","article-title":"A novel counterfeit feature extraction technique for exposing face-swap images based on deep learning and error level analysis","volume":"22","author":"Zhang","year":"2020","journal-title":"Entropy"},{"key":"10.1016\/j.dim.2025.100099_b147","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Wang, P., & Lu, W. (2020). Detecting deepfake video by learning two-level features with two-stream convolutional neural network. In Proceedings of the 2020 6th international conference on computing and artificial intelligence (pp. 291\u2013297).","DOI":"10.1145\/3404555.3404564"},{"issue":"12","key":"10.1016\/j.dim.2025.100099_b148","doi-asserted-by":"crossref","first-page":"1692","DOI":"10.3390\/e23121692","article-title":"MFF-net: Deepfake detection network based on multi-feature fusion","volume":"23","author":"Zhao","year":"2021","journal-title":"Entropy"},{"key":"10.1016\/j.dim.2025.100099_b149","doi-asserted-by":"crossref","unstructured":"Zi, B., Chang, M., Chen, J., Ma, X., & Jiang, Y.-G. (2020). Wilddeepfake: A challenging real-world dataset for deepfake detection. In Proceedings of the 28th ACM international conference on multimedia (pp. 2382\u20132390).","DOI":"10.1145\/3394171.3413769"}],"container-title":["Data and Information Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2543925125000075?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2543925125000075?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T08:09:05Z","timestamp":1765354145000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2543925125000075"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":149,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["S2543925125000075"],"URL":"https:\/\/doi.org\/10.1016\/j.dim.2025.100099","relation":{},"ISSN":["2543-9251"],"issn-type":[{"value":"2543-9251","type":"print"}],"subject":[],"published":{"date-parts":[[2025,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DeepFake video detection: Insights into model generalisation \u2014 A Systematic review","name":"articletitle","label":"Article Title"},{"value":"Data and Information Management","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.dim.2025.100099","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Authors. Published by Elsevier Ltd on behalf of School of Information Management Wuhan University.","name":"copyright","label":"Copyright"}],"article-number":"100099"}}