{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T17:03:10Z","timestamp":1780074190147,"version":"3.54.0"},"reference-count":50,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T00:00:00Z","timestamp":1780012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MOE (Ministry of Education in China) Liberal Arts and Social Sciences Foundation","award":["23YJCZH336"],"award-info":[{"award-number":["23YJCZH336"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Deepfake detection has become an essential task for ensuring the authenticity and security of digital media. Although recent approaches have achieved notable progress, most existing detectors still exhibit limited generalization to unseen forgery techniques and remain vulnerable to common perturbations such as compression, noise, and adversarial attacks. To overcome these issues, we propose Weakly Supervised EfficientNet Augmented Face Forgery Detector (WAFF), a novel framework that integrates fine-grained per-frame analysis with adaptive video-level fusion. Specifically, WAFF integrates WSEffiNet, an EfficientNet-B3-based backbone enhanced with a Weakly Supervised Data Augmentation Network (WS-DAN). This design generates attention maps to emphasize subtle facial forgery artifacts while encouraging complementary local\u2013global feature learning. At the video level, WAFF incorporates a multi-strategy fusion scheme that combines fake-frame counting, confidence averaging, and attention-guided voting to strike a balance between sensitivity and stability. Extensive experiments on FaceForensics++, Celeb-DF v2, DFD, DFDC, and FFIW-10K demonstrate that WAFF can achieve state-of-the-art performance under both high- and low-quality compression, while also enhancing cross-dataset generalization.<\/jats:p>","DOI":"10.3390\/jimaging12060240","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T15:57:18Z","timestamp":1780070238000},"page":"240","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["WAFF: A Synergetic Face Forgery Video Detection Method via Weakly Supervised EfficientNet"],"prefix":"10.3390","volume":"12","author":[{"given":"Zhengzhuo","family":"Pan","sequence":"first","affiliation":[{"name":"School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bohan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Zhongnan University of Economics and Law, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Longxiang","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Zhongnan University of Economics and Law, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dawei","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Zhongnan University of Economics and Law, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Zhongnan University of Economics and Law, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yudi","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Zhongnan University of Economics and Law, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Amerini, I., Barni, M., Battiato, S., Bestagini, P., Boato, G., Bruni, V., Caldelli, R., De Natale, F., De Nicola, R., and Guarnera, L. (2025). Deepfake media forensics: Status and future challenges. J. Imaging, 11.","DOI":"10.3390\/jimaging11030073"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.neucom.2022.09.135","article-title":"A comprehensive overview of Deepfake: Generation, detection, datasets, and opportunities","volume":"513","author":"Seow","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kumar, N., and Kundu, A. (2024). Securevision: Advanced cybersecurity deepfake detection with big data analytics. Sensors, 24.","DOI":"10.3390\/s24196300"},{"key":"ref_4","first-page":"1","article-title":"Deepfake detection: A comprehensive survey from the reliability perspective","volume":"57","author":"Wang","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"ref_5","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 Comput. Surv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.inffus.2020.06.014","article-title":"Deepfakes and beyond: A survey of face manipulation and fake detection","volume":"64","author":"Tolosana","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3536426","article-title":"Deepfake video detection via predictive representation learning","volume":"18","author":"Ge","year":"2022","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Akhtar, Z. (2023). Deepfakes generation and detection: A short survey. J. Imaging, 9.","DOI":"10.3390\/jimaging9010018"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4046","DOI":"10.1109\/TIFS.2023.3290752","article-title":"Dynamic difference learning with spatio\u2013temporal correlation for deepfake video detection","volume":"18","author":"Yin","year":"2023","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Larue, N., Vu, N.S., Struc, V., Peer, P., and Christophides, V. (2023, January 1\u20136). Seeable: Soft discrepancies and bounded contrastive learning for exposing deepfakes. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.01921"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Song, L., Fang, Z., Li, X., Dong, X., Jin, Z., Chen, Y., and Lyu, S. (2022, January 23\u201327). Adaptive face forgery detection in cross domain. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-19830-4_27"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gu, Z., Yao, T., Chen, Y., Yi, R., Ding, S., and Ma, L. (2022, January 23\u201329). Region-Aware Temporal Inconsistency Learning for DeepFake Video Detection. Proceedings of the IJCAI, Vienna, Austria.","DOI":"10.24963\/ijcai.2022\/129"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/TIP.2024.3441821","article-title":"Generalizable deepfake detection with phase-based motion analysis","volume":"34","author":"Prashnani","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e42273","DOI":"10.1016\/j.heliyon.2025.e42273","article-title":"Exploring autonomous methods for deepfake detection: A detailed survey on techniques and evaluation","volume":"11","author":"Sunil","year":"2025","journal-title":"Heliyon"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"110077","DOI":"10.1016\/j.patcog.2023.110077","article-title":"Deepfake detection via inter-frame inconsistency recomposition and enhancement","volume":"147","author":"Zhu","year":"2024","journal-title":"Pattern Recognit."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yang, R., You, K., Pang, C., Luo, X., and Lan, R. (2024). CSTAN: A deepfake detection network with CST attention for superior generalization. Sensors, 24.","DOI":"10.22541\/au.172156205.59104721\/v1"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lin, C.Y., Lee, J.C., Wang, S.J., Chiang, C.S., and Chou, C.L. (2024). Video detection method based on temporal and spatial foundations for accurate verification of authenticity. Electronics, 13.","DOI":"10.3390\/electronics13112132"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5801","DOI":"10.1007\/s11263-024-02136-1","article-title":"FD-GAN: Generalizable and robust forgery detection via generative adversarial networks","volume":"132","author":"Xu","year":"2024","journal-title":"Int. J. Comput. Vis."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6573","DOI":"10.1109\/TIFS.2024.3417266","article-title":"Exploring Bi-Level Inconsistency via Blended Images for Generalizable Face Forgery Detection","volume":"19","author":"Jiang","year":"2024","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1109\/TIFS.2024.3516561","article-title":"Attention consistency refined masked frequency forgery representation for generalizing face forgery detection","volume":"20","author":"Liu","year":"2024","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_21","unstructured":"Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., and Nie\u00dfner, M. (November, January 27). Faceforensics++: Learning to detect manipulated facial images. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_22","unstructured":"Nirkin, Y., Keller, Y., and Hassner, T. (November, January 27). Fsgan: Subject agnostic face swapping and reenactment. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Shiohara, K., Yang, X., and Taketomi, T. (2023, January 1\u20136). Blendface: Re-designing identity encoders for face-swapping. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00702"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, L., Bao, J., Yang, H., Chen, D., and Wen, F. (2020, January 13\u201319). Advancing high fidelity identity swapping for forgery detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00512"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C., and Nie\u00dfner, M. (2016, January 27\u201330). Face2face: Real-time face capture and reenactment of rgb videos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.262"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yang, K., Chen, K., Guo, D., Zhang, S.H., Guo, Y.C., and Zhang, W. (2022, January 23\u201327). Face2face \u03c1: Real-time high-resolution one-shot face reenactment. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-19778-9_4"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3306346.3323035","article-title":"Deferred neural rendering: Image synthesis using neural textures","volume":"38","author":"Thies","year":"2019","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_28","unstructured":"Ye, Z., Sun, Z., Wen, Y.H., Sun, Y., Lv, T., Yi, R., and Liu, Y.J. (2022). Dynamic neural textures: Generating talking-face videos with continuously controllable expressions. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"8348","DOI":"10.1109\/TPAMI.2024.3404334","article-title":"Learning disentangled representation for one-shot progressive face swapping","volume":"46","author":"Li","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Luo, Y., Zhu, J., He, K., Chu, W., Tai, Y., Wang, C., and Yan, J. (2022, January 23\u201327). Styleface: Towards identity-disentangled face generation on megapixels. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-19787-1_17"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Deng, Y., Wang, B., and Shum, H.Y. (2023, January 17\u201324). Learning detailed radiance manifolds for high-fidelity and 3d-consistent portrait synthesis from monocular image. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00430"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Thies, J., Elgharib, M., Tewari, A., Theobalt, C., and Nie\u00dfner, M. (2020, January 23\u201328). Neural voice puppetry: Audio-driven facial reenactment. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58517-4_42"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Gong, L.Y., and Li, X.J. (2024). A contemporary survey on deepfake detection: Datasets, algorithms, and challenges. Electronics, 13.","DOI":"10.3390\/electronics13030585"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2621","DOI":"10.3934\/era.2024119","article-title":"Analyzing temporal coherence for deepfake video detection","volume":"32","author":"Amin","year":"2024","journal-title":"Electron. Res. Arch."},{"key":"ref_35","first-page":"100099","article-title":"DeepFake video detection: Insights into model generalisation\u2014A Systematic review","volume":"9","author":"Ramanaharan","year":"2025","journal-title":"Data Inf. Manag."},{"key":"ref_36","unstructured":"Li, Y., and Lyu, S. (2018). Exposing deepfake videos by detecting face warping artifacts. arXiv."},{"key":"ref_37","unstructured":"Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., and Guo, B. (2023, January 17\u201324). Face x-ray for more general face forgery detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Luo, Y., Zhang, Y., Yan, J., and Liu, W. (2021, January 20\u201325). Generalizing face forgery detection with high-frequency features. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01605"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Dong, S., Wang, J., Ji, R., Liang, J., Fan, H., and Ge, Z. (2023, January 17\u201324). Implicit identity leakage: The stumbling block to improving deepfake detection generalization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00389"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhang, Y., Song, Y., Liu, L., and Wang, J. (2022, January 19\u201320). Self-supervised learning of adversarial example: Towards good generalizations for deepfake detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01815"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., and Yu, N. (2022, January 23\u201327). UIA-ViT: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-20065-6_23"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Masi, I., Killekar, A., Mascarenhas, R.M., Gurudatt, S.P., and AbdAlmageed, W. (2020, January 23\u201328). Two-branch recurrent network for isolating deepfakes in videos. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58571-6_39"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhou, T., Wang, W., Liang, Z., and Shen, J. (2021, January 20\u201325). Face forensics in the wild. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00572"},{"key":"ref_44","unstructured":"Hu, J., Liao, X., Liang, J., Zhou, W., and Qin, Z. (March, January 22). Finfer: Frame inference-based deepfake detection for high-visual-quality videos. Proceedings of the AAAI Conference on Artificial Intelligence, Online."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e1520","DOI":"10.1002\/widm.1520","article-title":"Deepfake detection using deep learning methods: A systematic and comprehensive review","volume":"14","author":"Heidari","year":"2024","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Raza, A., Basit, A., Amin, A., Arfeen, Z.A., Masud, M.I., Fayyaz, U., and Jumani, T.A. (2026). A comprehensive review of deepfake detection techniques: From traditional machine learning to advanced deep learning architectures. AI, 7.","DOI":"10.3390\/ai7020068"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1007\/s12559-024-10255-7","article-title":"A novel blockchain-based deepfake detection method using federated and deep learning models","volume":"16","author":"Heidari","year":"2024","journal-title":"Cogn. Comput."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., and Yu, N. (2021, January 20\u201325). Multi-attentional deepfake detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00222"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Qian, Y., Yin, G., Sheng, L., Chen, Z., and Shao, J. (2020, January 23\u201328). Thinking in frequency: Face forgery detection by mining frequency-aware clues. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58610-2_6"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Liu, H., Li, X., Zhou, W., Chen, Y., He, Y., Xue, H., Zhang, W., and Yu, N. (2021, January 20\u201325). Spatial-phase shallow learning: Rethinking face forgery detection in frequency domain. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00083"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/6\/240\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T16:17:39Z","timestamp":1780071459000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/6\/240"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,29]]},"references-count":50,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["jimaging12060240"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12060240","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,29]]}}}