{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T17:21:41Z","timestamp":1779384101399,"version":"3.53.1"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T00:00:00Z","timestamp":1751846400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T00:00:00Z","timestamp":1751846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Gansu Provincial Department of Education Industrial Support Plan Project under Grant","award":["2022CYZC-16"],"award-info":[{"award-number":["2022CYZC-16"]}]},{"name":"the National Natural Science Foundation of China under Grant","award":["62267007"],"award-info":[{"award-number":["62267007"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s00530-025-01893-4","type":"journal-article","created":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T09:04:10Z","timestamp":1751879050000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Generalizable face forgery detection based on adaptive spatial-frequency information mining"],"prefix":"10.1007","volume":"31","author":[{"given":"Yongfeng","family":"Qi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongli","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yajuan","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanzhe","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haixi","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,7]]},"reference":[{"key":"1893_CR1","doi-asserted-by":"crossref","unstructured":"Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: 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 (2020)","DOI":"10.1109\/CVPR42600.2020.00505"},{"key":"1893_CR2","unstructured":"Zhu, H., Huang, H., Li, Y., Zheng, A., He, R.: Arbitrary talking face generation via attentional audio-visual coherence learning. arXiv preprint arXiv:1812.06589 (2018)"},{"key":"1893_CR3","doi-asserted-by":"crossref","unstructured":"Wu, R., Zhang, G., Lu, S., Chen, T.: Cascade ef-gan: Progressive facial expression editing with local focuses. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5021\u20135030 (2020)","DOI":"10.1109\/CVPR42600.2020.00507"},{"key":"1893_CR4","doi-asserted-by":"crossref","unstructured":"He, Y., Yu, N., Keuper, M., Fritz, M.: Beyond the spectrum: Detecting deepfakes via re-synthesis. arXiv preprint arXiv:2105.14376 (2021)","DOI":"10.24963\/ijcai.2021\/349"},{"key":"1893_CR5","doi-asserted-by":"crossref","unstructured":"Hu, Z., Xie, H., Wang, Y., Li, J., Wang, Z., Zhang, Y.: Dynamic inconsistency-aware deepfake video detection. In: IJCAI, pp. 736\u2013742 (2021)","DOI":"10.24963\/ijcai.2021\/102"},{"issue":"10","key":"1893_CR6","doi-asserted-by":"publisher","first-page":"6111","DOI":"10.1109\/TPAMI.2021.3093446","volume":"44","author":"Y Nirkin","year":"2021","unstructured":"Nirkin, Y., Wolf, L., Keller, Y., Hassner, T.: Deepfake detection based on discrepancies between faces and their context. IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 6111\u20136121 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1893_CR7","doi-asserted-by":"crossref","unstructured":"Dong, S., Wang, J., Ji, R., Liang, J., Fan, H., Ge, Z.: Implicit identity leakage: The stumbling block to improving deepfake detection generalization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3994\u20134004 (2023)","DOI":"10.1109\/CVPR52729.2023.00389"},{"key":"1893_CR8","doi-asserted-by":"crossref","unstructured":"Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8261\u20138265 (2019). IEEE","DOI":"10.1109\/ICASSP.2019.8683164"},{"key":"1893_CR9","doi-asserted-by":"crossref","unstructured":"Li, Y., Chang, M.-C., Lyu, S.: In ictu oculi: Exposing ai created fake videos by detecting eye blinking. In: 2018 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1\u20137 (2018). IEEE","DOI":"10.1109\/WIFS.2018.8630787"},{"key":"1893_CR10","doi-asserted-by":"crossref","unstructured":"Dang, H., Liu, F., Stehouwer, J., Liu, X., Jain, A.K.: On the detection of digital face manipulation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5781\u20135790 (2020)","DOI":"10.1109\/CVPR42600.2020.00582"},{"key":"1893_CR11","doi-asserted-by":"crossref","unstructured":"Masi, I., Killekar, A., Mascarenhas, R.M., Gurudatt, S.P., AbdAlmageed, W.: Two-branch recurrent network for isolating deepfakes in videos. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part VII 16, pp. 667\u2013684 (2020). Springer","DOI":"10.1007\/978-3-030-58571-6_39"},{"key":"1893_CR12","doi-asserted-by":"crossref","unstructured":"Qi, H., Guo, Q., Juefei-Xu, F., Xie, X., Ma, L., Feng, W., Liu, Y., Zhao, J.: Deeprhythm: Exposing deepfakes with attentional visual heartbeat rhythms. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 4318\u20134327 (2020)","DOI":"10.1145\/3394171.3413707"},{"key":"1893_CR13","first-page":"3022","volume":"33","author":"T Dzanic","year":"2020","unstructured":"Dzanic, T., Shah, K., Witherden, F.: Fourier spectrum discrepancies in deep network generated images. Adv. Neural. Inf. Process. Syst. 33, 3022\u20133032 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1893_CR14","doi-asserted-by":"publisher","first-page":"4234","DOI":"10.1109\/TIFS.2021.3102487","volume":"16","author":"J Yang","year":"2021","unstructured":"Yang, J., Li, A., Xiao, S., Lu, W., Gao, X.: Mtd-net: Learning to detect deepfakes images by multi-scale texture difference. IEEE Trans. Inf. Forensics Secur. 16, 4234\u20134245 (2021)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"1893_CR15","doi-asserted-by":"publisher","first-page":"3008","DOI":"10.1109\/TIFS.2022.3198275","volume":"17","author":"C Miao","year":"2022","unstructured":"Miao, C., Tan, Z., Chu, Q., Yu, N., Guo, G.: Hierarchical frequency-assisted interactive networks for face manipulation detection. IEEE Trans. Inf. Forensics Secur. 17, 3008\u20133021 (2022)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"1893_CR16","doi-asserted-by":"crossref","unstructured":"Qian, Y., Yin, G., Sheng, L., Chen, Z., Shao, J.: Thinking in frequency: Face forgery detection by mining frequency-aware clues. In: European Conference on Computer Vision, pp. 86\u2013103 (2020). Springer","DOI":"10.1007\/978-3-030-58610-2_6"},{"key":"1893_CR17","doi-asserted-by":"crossref","unstructured":"Liu, H., Li, X., Zhou, W., Chen, Y., He, Y., Xue, H., Zhang, W., Yu, N.: Spatial-phase shallow learning: rethinking face forgery detection in frequency domain. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 772\u2013781 (2021)","DOI":"10.1109\/CVPR46437.2021.00083"},{"key":"1893_CR18","first-page":"735","volume":"36","author":"Q Gu","year":"2022","unstructured":"Gu, Q., Chen, S., Yao, T., Chen, Y., Ding, S., Yi, R.: Exploiting fine-grained face forgery clues via progressive enhancement learning. Proc. AAAI Conf. Artif. Intell. 36, 735\u2013743 (2022)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"issue":"1","key":"1893_CR19","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/T-C.1974.223784","volume":"100","author":"N Ahmed","year":"1974","unstructured":"Ahmed, N., Natarajan, T., Rao, K.R.: Discrete cosine transform. IEEE Trans. Comput. 100(1), 90\u201393 (1974)","journal-title":"IEEE Trans. Comput."},{"key":"1893_CR20","first-page":"1081","volume":"35","author":"S Chen","year":"2021","unstructured":"Chen, S., Yao, T., Chen, Y., Ding, S., Li, J., Ji, R.: Local relation learning for face forgery detection. Proc. AAAI Conf. Artif. Intell. 35, 1081\u20131088 (2021)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"1893_CR21","unstructured":"Chen, Z., Yang, H.: Manipulated face detector: Joint spatial and frequency domain attention network. 1(2), 4 (2020) arXiv preprint arXiv:2005.02958"},{"key":"1893_CR22","doi-asserted-by":"crossref","unstructured":"Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16317\u201316326 (2021)","DOI":"10.1109\/CVPR46437.2021.01605"},{"key":"1893_CR23","doi-asserted-by":"crossref","unstructured":"Haliassos, A., Vougioukas, K., Petridis, S., Pantic, M.: 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 (2021)","DOI":"10.1109\/CVPR46437.2021.00500"},{"key":"1893_CR24","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhang, Y., Song, Y., Liu, L., Wang, J.: Self-supervised learning of adversarial example: Towards good generalizations for deepfake detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 18710\u201318719 (2022)","DOI":"10.1109\/CVPR52688.2022.01815"},{"key":"1893_CR25","doi-asserted-by":"crossref","unstructured":"Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1001\u20131012 (2021)","DOI":"10.1109\/CVPRW53098.2021.00111"},{"key":"1893_CR26","first-page":"2316","volume":"36","author":"K Sun","year":"2022","unstructured":"Sun, K., Yao, T., Chen, S., Ding, S., Li, J., Ji, R.: Dual contrastive learning for general face forgery detection. Proc. AAAI Conf. Artif. Intell. 36, 2316\u20132324 (2022)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"1893_CR27","unstructured":"Cozzolino, D., Thies, J., R\u00f6ssler, A., Riess, C., Nie\u00dfner, M., Verdoliva, L.: Forensictransfer: Weakly-supervised domain adaptation for forgery detection. arXiv preprint arXiv:1812.02510 (2018)"},{"issue":"5","key":"1893_CR28","doi-asserted-by":"publisher","first-page":"1566","DOI":"10.1109\/TIFS.2012.2202227","volume":"7","author":"P Ferrara","year":"2012","unstructured":"Ferrara, P., Bianchi, T., De Rosa, A., Piva, A.: Image forgery localization via fine-grained analysis of cfa artifacts. IEEE Trans. Inf. Forensics Secur. 7(5), 1566\u20131577 (2012)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"3","key":"1893_CR29","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1109\/TIFS.2012.2190402","volume":"7","author":"J Fridrich","year":"2012","unstructured":"Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7(3), 868\u2013882 (2012)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"1893_CR30","doi-asserted-by":"crossref","unstructured":"Li, J., Xie, H., Li, J., Wang, Z., Zhang, Y.: Frequency-aware discriminative feature learning supervised by single-center loss for face forgery detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6458\u20136467 (2021)","DOI":"10.1109\/CVPR46437.2021.00639"},{"key":"1893_CR31","doi-asserted-by":"crossref","unstructured":"Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2185\u20132194 (2021)","DOI":"10.1109\/CVPR46437.2021.00222"},{"key":"1893_CR32","doi-asserted-by":"crossref","unstructured":"Gu, Q., Chen, S., Yao, T., Chen, Y., Ding, S., Yi, R.: Exploiting fine-grained face forgery clues via progressive enhancement learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 735\u2013743 (2022)","DOI":"10.1609\/aaai.v36i1.19954"},{"key":"1893_CR33","doi-asserted-by":"crossref","unstructured":"Shiohara, K., Yamasaki, T.: Detecting deepfakes with self-blended images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 18720\u201318729 (2022)","DOI":"10.1109\/CVPR52688.2022.01816"},{"key":"1893_CR34","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1109\/TIFS.2022.3146781","volume":"17","author":"P Yu","year":"2022","unstructured":"Yu, P., Fei, J., Xia, Z., Zhou, Z., Weng, J.: Improving generalization by commonality learning in face forgery detection. IEEE Trans. Inf. Forensics Secur. 17, 547\u2013558 (2022)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"1","key":"1893_CR35","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.1177\/03611981241258753","volume":"2679","author":"X Dong","year":"2025","unstructured":"Dong, X., Shi, P., Liang, T., Yang, A.: Ctaffnet: Cnn\u2013transformer adaptive feature fusion object detection algorithm for complex traffic scenarios. Transp. Res. Rec. 2679(1), 1947\u20131965 (2025)","journal-title":"Transp. Res. Rec."},{"key":"1893_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.displa.2024.102814","volume":"84","author":"X Dong","year":"2024","unstructured":"Dong, X., Shi, P., Qi, H., Yang, A., Liang, T.: Ts-bev: Bev object detection algorithm based on temporal-spatial feature fusion. Displays 84, 102814 (2024)","journal-title":"Displays"},{"key":"1893_CR37","doi-asserted-by":"crossref","unstructured":"Fei, J., Dai, Y., Yu, P., Shen, T., Xia, Z., Weng, J.: Learning second order local anomaly for general face forgery detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20270\u201320280 (2022)","DOI":"10.1109\/CVPR52688.2022.01963"},{"issue":"7","key":"1893_CR38","doi-asserted-by":"publisher","first-page":"926","DOI":"10.1109\/LSP.2018.2822810","volume":"25","author":"F Wang","year":"2018","unstructured":"Wang, F., Cheng, J., Liu, W., Liu, H.: Additive margin softmax for face verification. IEEE Signal Process. Lett. 25(7), 926\u2013930 (2018)","journal-title":"IEEE Signal Process. Lett."},{"key":"1893_CR39","doi-asserted-by":"crossref","unstructured":"Song, L., Gong, D., Li, Z., Liu, C., Liu, W.: Occlusion robust face recognition based on mask learning with pairwise differential siamese network. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 773\u2013782 (2019)","DOI":"10.1109\/ICCV.2019.00086"},{"key":"1893_CR40","doi-asserted-by":"crossref","unstructured":"Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., Nie\u00dfner, M.: Faceforensics++: Learning to detect manipulated facial images. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1\u201311 (2019)","DOI":"10.1109\/ICCV.2019.00009"},{"key":"1893_CR41","doi-asserted-by":"crossref","unstructured":"Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: 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 (2020)","DOI":"10.1109\/CVPR42600.2020.00327"},{"key":"1893_CR42","unstructured":"Dufour, N., Gully, A.: Contributing Data to Deepfake Detection Research. Website. https:\/\/blog.research.google\/2019\/09\/contributing-data-to-deepfake-detection.html (2019)"},{"key":"1893_CR43","unstructured":"Dolhansky, B.: The dee pfake detection challenge (dfdc) pre view dataset. arXiv preprint arXiv:1910.08854 (2019)"},{"key":"1893_CR44","unstructured":"Tora, M.: Deepfakes. Website. https:\/\/github.com\/deepfakes\/faceswap\/tree\/v2.0.0 (2018)"},{"key":"1893_CR45","doi-asserted-by":"crossref","unstructured":"Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C., Nie\u00dfner, M.: Face2face: Real-time face capture and reenactment of rgb videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2387\u20132395 (2016)","DOI":"10.1109\/CVPR.2016.262"},{"key":"1893_CR46","unstructured":"Kowalski, M.: Faceswap. Website. https:\/\/github.com\/marekkowalski\/faceswap (2018)"},{"issue":"4","key":"1893_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3306346.3323035","volume":"38","author":"J Thies","year":"2019","unstructured":"Thies, J., Zollh\u00f6fer, M., Nie\u00dfner, M.: Deferred neural rendering: Image synthesis using neural textures. Acm Transactions on Graphics (TOG) 38(4), 1\u201312 (2019)","journal-title":"Acm Transactions on Graphics (TOG)"},{"key":"1893_CR48","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251\u20131258 (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"1893_CR49","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.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115, 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"1893_CR50","unstructured":"Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114 (2019). PMLR"},{"key":"1893_CR51","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.imavis.2016.01.002","volume":"47","author":"C Sagonas","year":"2016","unstructured":"Sagonas, C., Antonakos, E., Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: 300 faces in-the-wild challenge: Database and results. Image Vis. Comput. 47, 3\u201318 (2016)","journal-title":"Image Vis. Comput."},{"key":"1893_CR52","unstructured":"Diederik, P.K.: Adam: A method for stochastic optimization. arXiv preprint arXiv: 1412.6980 (2014)"},{"key":"1893_CR53","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","volume":"128","author":"RR Selvaraju","year":"2020","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. Int. J. Comput. Vision 128, 336\u2013359 (2020)","journal-title":"Int. J. Comput. Vision"},{"key":"1893_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2021.103170","volume":"204","author":"Z Guo","year":"2021","unstructured":"Guo, Z., Yang, G., Chen, J., Sun, X.: Fake face detection via adaptive manipulation traces extraction network. Comput. Vis. Image Underst. 204, 103170 (2021)","journal-title":"Comput. Vis. Image Underst."},{"key":"1893_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119361","volume":"215","author":"Z Guo","year":"2023","unstructured":"Guo, Z., Yang, G., Zhang, D., Xia, M.: Rethinking gradient operator for exposing ai-enabled face forgeries. Expert Syst. Appl. 215, 119361 (2023)","journal-title":"Expert Syst. Appl."},{"key":"1893_CR56","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1109\/TIFS.2023.3324739","volume":"19","author":"Z Guo","year":"2023","unstructured":"Guo, Z., Jia, Z., Wang, L., Wang, D., Yang, G., Kasabov, N.: Constructing new backbone networks via space-frequency interactive convolution for deepfake detection. IEEE Trans. Inf. Forensics Secur. 19, 401\u2013413 (2023)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"1893_CR57","doi-asserted-by":"crossref","unstructured":"Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 2382\u20132390 (2020)","DOI":"10.1145\/3394171.3413769"},{"issue":"16","key":"1893_CR58","doi-asserted-by":"publisher","first-page":"19007","DOI":"10.1007\/s10489-023-04462-2","volume":"53","author":"Y Zhao","year":"2023","unstructured":"Zhao, Y., Jin, X., Gao, S., Wu, L., Yao, S., Jiang, Q.: Tan-gfd: generalizing face forgery detection based on texture information and adaptive noise mining. Appl. Intell. 53(16), 19007\u201319027 (2023)","journal-title":"Appl. Intell."},{"key":"1893_CR59","doi-asserted-by":"crossref","unstructured":"Wang, J., Wu, Z., Ouyang, W., Han, X., Chen, J., Jiang, Y.-G., Li, S.-N.: M2tr: Multi-modal multi-scale transformers for deepfake detection. In: Proceedings of the 2022 International Conference on Multimedia Retrieval, pp. 615\u2013623 (2022)","DOI":"10.1145\/3512527.3531415"},{"key":"1893_CR60","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1016\/j.ins.2022.03.026","volume":"596","author":"G Wang","year":"2022","unstructured":"Wang, G., Jiang, Q., Jin, X., Cui, X.: Ffr_fd: Effective and fast detection of deepfakes via feature point defects. Inf. Sci. 596, 472\u2013488 (2022)","journal-title":"Inf. Sci."},{"issue":"7","key":"1893_CR61","doi-asserted-by":"publisher","first-page":"9366","DOI":"10.1109\/TNNLS.2022.3233063","volume":"35","author":"W Lu","year":"2023","unstructured":"Lu, W., Liu, L., Zhang, B., Luo, J., Zhao, X., Zhou, Y., Huang, J.: Detection of deepfake videos using long-distance attention. IEEE Trans. Neural Netw. Learn. Syst. 35(7), 9366\u20139379 (2023)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-01893-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-025-01893-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-01893-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T09:05:27Z","timestamp":1757927127000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-025-01893-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,7]]},"references-count":61,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["1893"],"URL":"https:\/\/doi.org\/10.1007\/s00530-025-01893-4","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,7]]},"assertion":[{"value":"20 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 July 2025","order":3,"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 known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}],"article-number":"298"}}