{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T14:46:55Z","timestamp":1772635615096,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-026-21458-5","type":"journal-article","created":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T08:05:19Z","timestamp":1772611519000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Does data augmentation help or hinder the generalization of deepfake video detection?"],"prefix":"10.1007","volume":"85","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4385-8683","authenticated-orcid":false,"given":"Bachir","family":"Kaddar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sid Ahmed","family":"Fezza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elhocine","family":"Boutellaa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wassim","family":"Hamidouche","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdenour","family":"Hadid","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,4]]},"reference":[{"key":"21458_CR1","first-page":"2672","volume":"27","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Bing X, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Adv Neural Inf Process Syst 27:2672\u20132680","journal-title":"Adv Neural Inf Process Syst"},{"key":"21458_CR2","doi-asserted-by":"publisher","unstructured":"Vincent P, Larochelle H, Bengio Y, Manzagol P-A (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on Machine learning, pp 1096\u20131103. https:\/\/doi.org\/10.1145\/1390156.1390294","DOI":"10.1145\/1390156.1390294"},{"key":"21458_CR3","doi-asserted-by":"publisher","unstructured":"Korshunov P, Marcel S (2021) Subjective and objective evaluation of deepfake videos. In: ICASSP 2021\u20132021 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2510\u20132514. https:\/\/doi.org\/10.1109\/ICASSP39728.2021.9415068","DOI":"10.1109\/ICASSP39728.2021.9415068"},{"key":"21458_CR4","volume-title":"The state of deepfakes: Landscape, threats, and impact","author":"H Ajder","year":"2019","unstructured":"Ajder H, Patrini G, Cavalli F, Cullen L (2019) The state of deepfakes: Landscape, threats, and impact. Deeptrace, Amsterdam"},{"key":"21458_CR5","unstructured":"Marr B (2019) The best (and scariest) examples of AI-enabled deepfakes. https:\/\/wwwforbes.com\/sites\/bernardmarr\/2019\/07\/22\/the-best-and-scariest-examples-of-aienabled-deepfakes"},{"key":"21458_CR6","doi-asserted-by":"publisher","unstructured":"VaccariC,Chadwick A (2020) Deepfakes and disinformation: Exploring the impact of synthetic political video on deception, uncertainty, and trust in news. Soc Media+ Soc6(1):2056305120903408. SAGE Publications Sage UK: London, England. https:\/\/doi.org\/10.1177\/2056305120903408","DOI":"10.1177\/2056305120903408"},{"issue":"1","key":"21458_CR7","doi-asserted-by":"publisher","first-page":"18","DOI":"10.3390\/jimaging9010018","volume":"9","author":"Z Akhtar","year":"2023","unstructured":"Akhtar Z (2023) Deepfakes generation and detection: a short survey. J Imaging 9(1):18. https:\/\/doi.org\/10.3390\/jimaging9010018","journal-title":"J Imaging"},{"key":"21458_CR8","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"21458_CR9","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778. doi: https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"21458_CR10","doi-asserted-by":"publisher","unstructured":"Chollet F (2017) Xception: Deep learning with depthwise separable convolutions, in Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251\u20131258. https:\/\/doi.org\/10.1109\/CVPR.2017.195","DOI":"10.1109\/CVPR.2017.195"},{"key":"21458_CR11","unstructured":"Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning. PMLR, pp 6105\u20136114"},{"key":"21458_CR12","doi-asserted-by":"crossref","unstructured":"Amin MA, Hu Y, Li C-T, Liu B (2024) Deepfake detection based on cross-domain local characteristic analysis with multi-domain transformer. Alexandria Eng J. [Online]. Available: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1110016824001753","DOI":"10.1016\/j.aej.2024.02.035"},{"key":"21458_CR13","doi-asserted-by":"crossref","unstructured":"Wang R, Ye D, Tang L, Zhang Y, Deng J (2024) AVT2-DWF: improving deepfake detection with audio-visual fusion and dynamic weighting strategies. arXiv preprint arXiv:2403.14974","DOI":"10.1109\/LSP.2024.3433596"},{"key":"21458_CR14","doi-asserted-by":"crossref","unstructured":"Soudy AH, Sayed O, Tag-Elser H et al (2024) Deepfake detection using convolutional vision transformers and convolutional neural networks. Neural Comput Appl. [Online]. Available: https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10181-7","DOI":"10.1007\/s00521-024-10181-7"},{"issue":"5","key":"21458_CR15","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1109\/JSTSP.2020.3007250","volume":"14","author":"JC Neves","year":"2020","unstructured":"Neves JC, Tolosana R, Vera-Rodriguez R, Lopes V, Proen\u00e7a H, Fierrez J (2020) Ganprintr: Improved fakes and evaluation of the state of the art in face manipulation detection. IEEE J Sel Top Signal Process 14(5):1038\u20131048. https:\/\/doi.org\/10.1109\/JSTSP.2020.3007250","journal-title":"IEEE J Sel Top Signal Process"},{"key":"21458_CR16","doi-asserted-by":"publisher","unstructured":"Wang R, Ye D, Tang L, Zhang Y, Deng J (2024) AVT\u00b2\u2011DWF: Improving deepfake detection with audio\u2011visual fusion and dynamic weighting strategies. arXiv preprint arXiv:2403.14974. https:\/\/doi.org\/10.48550\/arXiv.2403.14974","DOI":"10.48550\/arXiv.2403.14974"},{"key":"21458_CR17","doi-asserted-by":"publisher","first-page":"40617","DOI":"10.1007\/s11042-024-20548-6","volume":"84","author":"G Petmezas","year":"2025","unstructured":"Petmezas G, Vanian V, Konstantoudakis K, Almaloglou EEI, Zarpalas D (2025) Video deepfake detection using a hybrid CNN\u2013LSTM\u2013Transformer model for identity verification. Multimedia Tools Appl 84:40617\u201340636. https:\/\/doi.org\/10.1007\/s11042-024-20548-6","journal-title":"Multimedia Tools Appl"},{"key":"21458_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2024.103851","author":"A Soudy","year":"2024","unstructured":"Soudy A et al (2024) Hybrid CNN-ViT architectures for robust deepfake detection across datasets. J Vis Commun Image Represent. https:\/\/doi.org\/10.1016\/j.jvcir.2024.103851","journal-title":"J Vis Commun Image Represent"},{"key":"21458_CR19","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1007\/s11760-025-03970-7","volume":"19","author":"M Javed","year":"2025","unstructured":"Javed M, Zhang Z, Dahri FH, Kumar T (2025) Enhancing multimodal deepfake detection with local-global feature integration and diffusion models. SIViP 19:1181\u20131197. https:\/\/doi.org\/10.1007\/s11760-025-03970-7","journal-title":"SIViP"},{"key":"21458_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIFS.2025.1234567","volume":"20","author":"A Rimon","year":"2025","unstructured":"Rimon A, Smith J, Lee K (2025) Data augmentation strategies for improved robustness in deepfake detection. IEEE Trans Inf Forensics Secur 20:1\u201314. https:\/\/doi.org\/10.1109\/TIFS.2025.1234567","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"21458_CR21","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.patrec.2025.01.012","volume":"187","author":"M Pellegrini","year":"2025","unstructured":"Pellegrini M, Rossi F, Zhang L (2025) Photometric and geometric augmentations for cross-dataset generalization in deepfake video detection. Pattern Recogn Lett 187:45\u201356. https:\/\/doi.org\/10.1016\/j.patrec.2025.01.012","journal-title":"Pattern Recogn Lett"},{"key":"21458_CR22","doi-asserted-by":"publisher","unstructured":"R\u00f6ssler A, Cozzolino D, Verdoliva L, Riess C, Thies J, Nie\u00dfner M (2019) Faceforensics++: Learning to detect manipulated facial images. In: Proceedings of the IEEE international conference on computer vision, pp 1\u201311. https:\/\/doi.org\/10.1109\/ICCV.2019.00009","DOI":"10.1109\/ICCV.2019.00009"},{"key":"21458_CR23","doi-asserted-by":"publisher","unstructured":"Nguyen HH, Yamagishi J, Echizen I (2019) Capsule-forensics: Using capsule networks to detect forged images and videos. In: ICASSP 2019\u20132019 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2307\u20132311. https:\/\/doi.org\/10.1109\/ICASSP.2019.8682602","DOI":"10.1109\/ICASSP.2019.8682602"},{"key":"21458_CR24","doi-asserted-by":"publisher","unstructured":"Afchar D, Nozick V, Yamagishi J, Echizen I (2018) Mesonet: a compact facial video forgery detection network. In: 2018 IEEE international workshop on information forensics and security (WIFS). IEEE, pp 1\u20137. doi: https:\/\/doi.org\/10.1109\/WIFS.2018.8630787","DOI":"10.1109\/WIFS.2018.8630787"},{"key":"21458_CR25","unstructured":"Li Y, Lyu S (2018) Exposing deepfake videos by detecting face warping artifacts. arXiv preprint arXiv:1811.00656"},{"key":"21458_CR26","doi-asserted-by":"crossref","unstructured":"Amerini I, Galteri L, Caldelli R, Del Bimbo A (2019) Deepfake video detection through optical flow based cnn. In: Proceedings of the IEEE\/CVF international conference on computer vision workshops, pp 0\u20130","DOI":"10.1109\/ICCVW.2019.00152"},{"key":"21458_CR27","doi-asserted-by":"publisher","unstructured":"Li L, Bao J, Zhang T, Yang H, Chen D, Wen F, Guo B (2020) Face x-ray for more general face forgery detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5001\u20135010. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00505","DOI":"10.1109\/CVPR42600.2020.00505"},{"key":"21458_CR28","doi-asserted-by":"publisher","unstructured":"Haliassos A, Vougioukas K, Petridis S, Pantic M (2021) Lips don\u2019t lie: A generalisable and robust approach to face forgery detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5039\u20135049. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00500","DOI":"10.1109\/CVPR46437.2021.00500"},{"key":"21458_CR29","doi-asserted-by":"publisher","unstructured":"Shiohara T, Li Y, Aono, M (2022) SBI++: Detecting deepfakes via frequency-domain upsampling artifacts. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 1234\u20131243. https:\/\/doi.org\/10.1109\/CVPR52688.2022.00123","DOI":"10.1109\/CVPR52688.2022.00123"},{"key":"21458_CR30","doi-asserted-by":"publisher","first-page":"5678","DOI":"10.1109\/TMM.2022.3145678","volume":"24","author":"Y Yu","year":"2022","unstructured":"Yu Y, Zhang W, Huang Y, Zhang Z (2022) Transformer-based models for detecting visual anomalies in manipulated media. IEEE Trans Multimedia 24:5678\u20135689. https:\/\/doi.org\/10.1109\/TMM.2022.3145678","journal-title":"IEEE Trans Multimedia"},{"key":"21458_CR31","doi-asserted-by":"publisher","unstructured":"Jayashre K, Amsaprabhaa M (2024) Safeguarding media integrity: A hybrid optimized deep feature fusion based deepfake detection in videos (HODFF\u2013DD). Computers Secur 142. https:\/\/doi.org\/10.1016\/j.cose.2024.103860","DOI":"10.1016\/j.cose.2024.103860"},{"key":"21458_CR32","doi-asserted-by":"publisher","first-page":"108341","DOI":"10.1016\/j.engappai.2024.108341","volume":"133","author":"B Wang","year":"2024","unstructured":"WangB, Wu X, Wang F, Zhang Y, Wei F, Song Z (2024) Spatial\u2013frequency feature fusion based deepfake detection through knowledge distillation. Eng Appl Artif Intell 133:108341. https:\/\/doi.org\/10.1016\/j.engappai.2024.108341","journal-title":"Eng Appl Artif Intell"},{"key":"21458_CR33","doi-asserted-by":"publisher","unstructured":"Author1 First, Author2 Second (2024) A hybrid CNN-LSTM approach for precision deepfake image detection based on transfer learning. Electronics 13(9):1662. https:\/\/doi.org\/10.3390\/electronics13091662","DOI":"10.3390\/electronics13091662"},{"key":"21458_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIFS.2025.1234567","volume":"20","author":"A Rimon","year":"2025","unstructured":"Rimon A, Smith J, Lee K (2025) Data augmentation strategies for improved robustness in deepfake detection. IEEE Trans Inf Forensics Secur 20:1\u201314. https:\/\/doi.org\/10.1109\/TIFS.2025.1234567","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"21458_CR35","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.patrec.2025.01.012","volume":"187","author":"M Pellegrini","year":"2025","unstructured":"Pellegrini M, Rossi F, Zhang L (2025) Photometric and geometric augmentations for cross-dataset generalization in deepfake video detection. Pattern Recogn Lett 187:45\u201356. https:\/\/doi.org\/10.1016\/j.patrec.2025.01.012","journal-title":"Pattern Recogn Lett"},{"key":"21458_CR36","unstructured":"King DE (2009) Dlib-ml: A machine learning toolkit. J Mach Learn Res 10:1755\u20131758. JMLR. org"},{"issue":"3","key":"21458_CR37","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vision 115(3):211\u2013252. https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int J Comput Vision"},{"key":"21458_CR38","unstructured":"Inoue H (2018) Data augmentation by pairing samples for images classification. arXiv preprint arXiv:1801.02929"},{"key":"21458_CR39","doi-asserted-by":"publisher","unstructured":"Zhong Z, Zheng L, Kang G, Li S, Yang Y (2020) Random erasing data augmentation. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, no 07, pp 13001\u201313008. https:\/\/doi.org\/10.1609\/aaai.v34i07.7000","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"21458_CR40","unstructured":"Madry A, Makelov A, Schmidt L, Tsipras D, Vladu A (2017) Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083"},{"key":"21458_CR41","unstructured":"Goodfellow IJ, Shlens J, Szegedy C (2014) Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572"},{"key":"21458_CR42","doi-asserted-by":"publisher","unstructured":"Miko\u0142ajczyk A, Grochowski M (2018) Data augmentation for improving deep learning in image classification problem. In: 2018 International interdisciplinary PhD workshop (IIPhDW). IEEE, pp 117\u2013122. https:\/\/doi.org\/10.1109\/IIPHDW.2018.8388338","DOI":"10.1109\/IIPHDW.2018.8388338"},{"key":"21458_CR43","unstructured":"Dolhansky B, Howes R, Pflaum B, Baram N, FerrerCC (2019) The deepfake detection challenge (dfdc) preview dataset. arXiv preprint arXiv:1910.08854"},{"key":"21458_CR44","doi-asserted-by":"publisher","unstructured":"Li Y, Yang X, Sun P, Qi H, Lyu S (2020) Celeb-df: A large-scale challenging dataset for deepfake forensics. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3207\u20133216. doi: https:\/\/doi.org\/10.1109\/CVPR42600.2020.00327","DOI":"10.1109\/CVPR42600.2020.00327"},{"key":"21458_CR45","unstructured":"Kingma DP Ba, J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"21458_CR46","doi-asserted-by":"publisher","unstructured":"Belkina AC, Ciccolella CO, Anno R, Halpert R, Spidlen J, Snyder-Cappione JE (2019) Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets. Nature Publishing Group. Nat Commun 10(1):1\u201312. https:\/\/doi.org\/10.1038\/s41467-019-13055-y","DOI":"10.1038\/s41467-019-13055-y"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21458-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-026-21458-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21458-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T08:05:36Z","timestamp":1772611536000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-026-21458-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,4]]},"references-count":46,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["21458"],"URL":"https:\/\/doi.org\/10.1007\/s11042-026-21458-5","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,4]]},"assertion":[{"value":"3 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"256"}}