{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T19:16:55Z","timestamp":1769282215938,"version":"3.49.0"},"reference-count":38,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T00:00:00Z","timestamp":1752796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Gansu Province Higher Education Institutions Industrial Support Program","award":["2020C-29"],"award-info":[{"award-number":["2020C-29"]}]},{"name":"Gansu Province Higher Education Institutions Industrial Support Program","award":["61562002"],"award-info":[{"award-number":["61562002"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2020C-29"],"award-info":[{"award-number":["2020C-29"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61562002"],"award-info":[{"award-number":["61562002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>When faced with diverse types of image tampering and image quality degradation in real-world scenarios, traditional image tampering localization methods often struggle to balance boundary accuracy and robustness. To address these issues, this paper proposes a symmetric guided dual-branch image tampering localization network\u2014FENet (Fusion-Enhanced Network)\u2014that integrates adaptive feature fusion and edge attention mechanisms. This method is based on a structurally symmetric dual-branch architecture, which extracts RGB semantic features and SRM noise residual information to comprehensively capture the fine-grained differences in tampered regions at the visual and statistical levels. To effectively fuse different features, this paper designs a self-calibrating fusion module (SCF), which introduces a content-aware dynamic weighting mechanism to adaptively adjust the importance of different feature branches, thereby enhancing the discriminative power and expressiveness of the fused features. Furthermore, considering that image tampering often involves abnormal changes in edge structures, we further propose an edge-aware coordinate attention mechanism (ECAM). By jointly modeling spatial position information and edge-guided information, the model is guided to focus more precisely on potential tampering boundaries, thereby enhancing its boundary detection and localization capabilities. Experiments on public datasets such as Columbia, CASIA, and NIST16 demonstrate that FENet achieves significantly better results than existing methods. We also analyze the model\u2019s performance under various image quality conditions, such as JPEG compression and Gaussian blur, demonstrating its robustness in real-world scenarios. Experiments in Facebook, Weibo, and WeChat scenarios show that our method achieves average F1 scores that are 2.8%, 3%, and 5.6% higher than those of existing state-of-the-art methods, respectively.<\/jats:p>","DOI":"10.3390\/sym17071150","type":"journal-article","created":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T10:10:38Z","timestamp":1752833438000},"page":"1150","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Symmetry-Guided Dual-Branch Network with Adaptive Feature Fusion and Edge-Aware Attention for Image Tampering Localization"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8441-9534","authenticated-orcid":false,"given":"Zhenxiang","family":"He","sequence":"first","affiliation":[{"name":"School of Cyberspace Security, Gansu University of Political Science and Law, Lanzhou 730070, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9078-1341","authenticated-orcid":false,"given":"Le","family":"Li","sequence":"additional","affiliation":[{"name":"School of Cyberspace Security, Gansu University of Political Science and Law, Lanzhou 730070, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0133-2129","authenticated-orcid":false,"given":"Hanbin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Cyberspace Security, Gansu University of Political Science and Law, Lanzhou 730070, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Guo, K., Zhu, H., and Cao, G. (2024, January 14\u201319). Effective image tampering localization via enhanced transformer and co-attention fusion. Proceedings of the ICASSP 2024\u20142024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Republic of Korea.","DOI":"10.1109\/ICASSP48485.2024.10446332"},{"key":"ref_2","unstructured":"Ma, X., Du, B., Jiang, Z., Hammadi, A.Y.A., and Zhou, J. (2023). IML-ViT: Benchmarking Image Manipulation Localization by Vision Transformer. arXiv."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chen, X., Dong, C., Ji, J., Cao, J., and Li, X. (2021, January 11\u201317). Image manipulation detection by multi-view multi-scale supervision. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01392"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhai, Y., Luan, T., Doermann, D., and Yuan, J. (2023, January 2\u20133). Towards generic image manipulation detection with weakly supervised self-consistency learning. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.02046"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Pan, X., Zhang, X., and Lyu, S. (2011, January 29\u201330). Exposing image forgery with blind noise estimation. Proceedings of the Thirteenth ACM Multimedia Workshop on Multimedia and Security, Buffalo, NY, USA.","DOI":"10.1145\/2037252.2037256"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1875","DOI":"10.1007\/s11263-022-01617-5","article-title":"Learning jpeg compression artifacts for image manipulation detection and localization","volume":"130","author":"Kwon","year":"2022","journal-title":"Int. J. Comput. Vis."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hsu, Y.F., and Chang, S.F. (2006, January 9\u201312). Detecting image splicing using geometry invariants and camera characteristics consistency. Proceedings of the 2006 IEEE International Conference on Multimedia and Expo, Toronto, ON, Canada.","DOI":"10.1109\/ICME.2006.262447"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wu, Y., AbdAlmageed, W., and Natarajan, P. (2019, January 15\u201320). Mantra-net: Manipulation tracing network for detection and localization of image forgeries with anomalous features. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00977"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"7505","DOI":"10.1109\/TCSVT.2022.3189545","article-title":"PSCC-Net: Progressive spatio-channel correlation network for image manipulation detection and localization","volume":"32","author":"Liu","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hu, X., Zhang, Z., Jiang, Z., Chaudhuri, S., Yang, Z., and Nevatia, R. (2020, January 23\u201328). SPAN: Spatial pyramid attention network for image manipulation localization. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK. Proceedings, Part XXI 16.","DOI":"10.1007\/978-3-030-58589-1_19"},{"key":"ref_11","unstructured":"Zhu, H., Cao, G., and Huang, X. (2023). Progressive feedback-enhanced transformer for image forgery localization. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1109\/TIFS.2012.2190402","article-title":"Rich models for steganalysis of digital images","volume":"7","author":"Fridrich","year":"2012","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1109\/TSP.2004.839932","article-title":"Exposing digital forgeries by detecting traces of resampling","volume":"53","author":"Popescu","year":"2005","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.1016\/j.imavis.2009.02.001","article-title":"Using noise inconsistencies for blind image forensics","volume":"27","author":"Mahdian","year":"2009","journal-title":"Image Vis. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3286","DOI":"10.1109\/TIP.2019.2895466","article-title":"Hybrid lstm and encoder\u2013decoder architecture for detection of image forgeries","volume":"28","author":"Bappy","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1109\/LSP.2023.3245947","article-title":"Learning traces by yourself: Blind image forgery localization via anomaly detection with ViT-VAE","volume":"30","author":"Chen","year":"2023","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1007\/s13042-023-02031-0","article-title":"DSSE-net: Dual stream skip edge-enhanced network with forgery loss for image forgery localization","volume":"15","author":"Zheng","year":"2024","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_18","first-page":"12077","article-title":"SegFormer: Simple and efficient design for semantic segmentation with transformers","volume":"34","author":"Xie","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1109\/TIFS.2022.3152362","article-title":"Self-adversarial training incorporating forgery attention for image forgery localization","volume":"17","author":"Zhuo","year":"2022","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yang, C., Wang, Z., Shen, H., Li, H., and Jiang, B. (2021, January 5\u20139). Multi-modality image manipulation detection. Proceedings of the 2021 IEEE International Conference on Multimedia and Expo (ICME), Shenzhen, China.","DOI":"10.1109\/ICME51207.2021.9428232"},{"key":"ref_21","unstructured":"Yu, F., and Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv."},{"key":"ref_22","unstructured":"Park, J., Woo, S., Lee, J.Y., and Kweon, I.S. (2018). Bam: Bottleneck attention module. arXiv."},{"key":"ref_23","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5029312","DOI":"10.1109\/TIM.2024.3436070","article-title":"EdgeFormer: Edge-aware Efficient Transformer for Image Super-resolution","volume":"73","author":"Luo","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 19\u201325). Coordinate attention for efficient mobile network design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wei, Q., Li, X., Yu, W., Zhang, X., Zhang, Y., Hu, B., Mo, B., Gong, D., Chen, N., and Ding, D. (2021, January 10\u201315). Learn to segment retinal lesions and beyond. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412088"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"103981","DOI":"10.1016\/j.jvcir.2023.103981","article-title":"Effective image tampering localization with multi-scale convnext feature fusion","volume":"98","author":"Zhu","year":"2024","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bappy, J.H., Roy-Chowdhury, A.K., Bunk, J., Nataraj, L., and Manjunath, B. (2017, January 22\u201329). Exploiting spatial structure for localizing manipulated image regions. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.532"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Dong, J., Wang, W., and Tan, T. (2013, January 6\u201310). Casia image tampering detection evaluation database. Proceedings of the 2013 IEEE China Summit and International Conference on Signal and Information Processing, Beijing, China.","DOI":"10.1109\/ChinaSIP.2013.6625374"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft COCO: Common Objects in Context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Guan, H., Kozak, M., Robertson, E., Lee, Y., Yates, A.N., Delgado, A., Zhou, D., Kheyrkhah, T., Smith, J., and Fiscus, J. (2019, January 7\u201311). MFC datasets: Large-scale benchmark datasets for media forensic challenge evaluation. Proceedings of the 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW), Waikoloa Village, HI, USA.","DOI":"10.1109\/WACVW.2019.00018"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1109\/TIFS.2013.2265677","article-title":"Exposing digital image forgeries by illumination color classification","volume":"8","author":"Riess","year":"2013","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Novozamsky, A., Mahdian, B., and Saic, S. (2020, January 1\u20135). IMD2020: A large-scale annotated dataset tailored for detecting manipulated images. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision Workshops, Snowmass, CO, USA.","DOI":"10.1109\/WACVW50321.2020.9096940"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2986","DOI":"10.1109\/TIFS.2021.3070444","article-title":"Image tampering localization using a dense fully convolutional network","volume":"16","author":"Zhuang","year":"2021","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3539","DOI":"10.1109\/TPAMI.2022.3180556","article-title":"Mvss-net: Multi-view multi-scale supervised networks for image manipulation detection","volume":"45","author":"Dong","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1109\/TIFS.2022.3144878","article-title":"Robust image forgery detection against transmission over online social networks","volume":"17","author":"Wu","year":"2022","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1109\/TIFS.2019.2916364","article-title":"Noiseprint: A CNN-based camera model fingerprint","volume":"15","author":"Cozzolino","year":"2019","journal-title":"IEEE Trans. Inf. Forensics Secur."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/7\/1150\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:11:57Z","timestamp":1760033517000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/7\/1150"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,18]]},"references-count":38,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["sym17071150"],"URL":"https:\/\/doi.org\/10.3390\/sym17071150","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,18]]}}}