{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:02:17Z","timestamp":1775066537373,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T00:00:00Z","timestamp":1743379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62166036"],"award-info":[{"award-number":["62166036"]}]},{"name":"National Natural Science Foundation of China","award":["2022B-152"],"award-info":[{"award-number":["2022B-152"]}]},{"name":"National Natural Science Foundation of China","award":["2025QNGR13"],"award-info":[{"award-number":["2025QNGR13"]}]},{"name":"Innovation Foundation of Gansu Provincial Department of Education","award":["62166036"],"award-info":[{"award-number":["62166036"]}]},{"name":"Innovation Foundation of Gansu Provincial Department of Education","award":["2022B-152"],"award-info":[{"award-number":["2022B-152"]}]},{"name":"Innovation Foundation of Gansu Provincial Department of Education","award":["2025QNGR13"],"award-info":[{"award-number":["2025QNGR13"]}]},{"name":"Gansu Province Talent Project","award":["62166036"],"award-info":[{"award-number":["62166036"]}]},{"name":"Gansu Province Talent Project","award":["2022B-152"],"award-info":[{"award-number":["2022B-152"]}]},{"name":"Gansu Province Talent Project","award":["2025QNGR13"],"award-info":[{"award-number":["2025QNGR13"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Existing artifact-based tampering detection methods face limitations when dealing with secondarily edited images. To address this, this paper proposes a novel semantic-based approach for detecting tampering in bilingual scene text images. Unlike existing artifact-based detection methods, this technique leverages the semantic consistency of bilingual text pairs. By translating both texts into a common language and calculating their semantic similarity, the image is identified as tampered with when the similarity falls below a threshold. Additionally, this paper introduces the Bilingual Scene Text Image Tampering Detection (BSTID) dataset. Experimental results demonstrate that the proposed method excels in detecting secondarily edited tampered images, achieving an average accuracy of 90.03% and an F1 score of 88.5%.<\/jats:p>","DOI":"10.3390\/sym17040536","type":"journal-article","created":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T05:43:33Z","timestamp":1743572613000},"page":"536","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Semantic-Based Conflict Detection: Tampering Detection Research in Bilingual Scene Images Containing Textual Content"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5380-8939","authenticated-orcid":false,"given":"Zhenjiang","family":"Li","sequence":"first","affiliation":[{"name":"School of Cyberspace Security, Gansu University of Political Science and Law, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8457-8169","authenticated-orcid":false,"given":"Jingzhe","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Cyberspace Security, Gansu University of Political Science and Law, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shu","family":"Wang","sequence":"additional","affiliation":[{"name":"Sichuan Institute of Computer Sciences, Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,31]]},"reference":[{"key":"ref_1","unstructured":"Zhang, Z.P. (2022). Research on Image Tampering Detection Based on Generative Adversarial Network, Chongqing University of Posts and Telecommunications."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, Z., and Sun, J. (2024, January 26\u201327). Image tampering detection in bilingual scenes based on semantic. Proceedings of the 2024 International Conference on Data Science and Network Security (ICDSNS), Tiptur, India.","DOI":"10.1109\/ICDSNS62112.2024.10691209"},{"key":"ref_3","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_4","first-page":"171","article-title":"Image tampering detection algorithm based on U-shaped detection network","volume":"40","author":"Wang","year":"2019","journal-title":"J. Commun."},{"key":"ref_5","first-page":"2987","article-title":"Image tampering detection algorithm based on cascaded convolutional neural network","volume":"41","author":"BI","year":"2019","journal-title":"J. Electron. Inf. Technol."},{"key":"ref_6","unstructured":"Yu, N., Davis, L.S., and Fritz, M. (November, January 27). Attributing fake images to GANs: Learning and analyzing GAN fingerprints. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, Z., Qi, X., and Torr, P.H.S. (2020, January 13\u201319). Global texture enhancement for fake face detection in the wild. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00808"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, X., Karaman, S., and Chang, S.-F. (2019, January 9\u201312). Detecting and simulating artifacts in gan fake images. Proceedings of the IEEE International Workshop on Information Forensics and Security (WIFS), Delft, The Netherlands.","DOI":"10.1109\/WIFS47025.2019.9035107"},{"key":"ref_9","unstructured":"Frank, J., Eisenhofer, T., Sch\u00f6nherr, L., Fischer, A., Kolossa, D., and Holz, T. (2020, January 13\u201318). Leveraging frequency analysis for deep fake image recognition. Proceedings of the International Conference on Machine Learning, PMLR, Virtual."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Rao, Y., and Ni, J. (2016, January 4\u20137). A deep learning approach to detection of splicing and copy-move forgeries in images. Proceedings of the 2016 IEEE International Workshop on Information Forensics and Security (WIFS), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/WIFS.2016.7823911"},{"key":"ref_11","first-page":"244","article-title":"Image tampering detection method based on approximate nearest neighbor search","volume":"57","author":"Wang","year":"2020","journal-title":"J. Adv. Lasers Optoelectron."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, B., and Pun, C.M. (2018, January 8\u201314). Deep fusion network for splicing forgery localization. Proceedings of the European Conference on Computer Vision (ECCV) Workshops, Munich, Germany.","DOI":"10.1007\/978-3-030-11012-3_21"},{"key":"ref_13","unstructured":"Vakkalanka, S., Mohan, C.K., Kumaraswamy, R., and Yegnanarayana, B. (2005, January 14\u201317). Combining multiple evidence for video classification. Proceedings of the 2005 International Conference on Intelligent Sensing and Information Processing, Bangalore, India."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yang, C., Li, H., Lin, F., Jiang, B., and Zhao, H. (2020, January 6\u201310). Constrained R-CNN: A general image manipulation detection model. Proceedings of the 2020 IEEE International Conference on Multimedia and Expo (ICME), London, UK.","DOI":"10.1109\/ICME46284.2020.9102825"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wu, Y., AbdAlmageed, W., and Natarajan, P. (2019, January 16\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_16","first-page":"217","article-title":"HRDA-Net: Image multiple manipulation detection and location algorithm in real scene","volume":"43","author":"Zhu","year":"2022","journal-title":"J. Commun. Tongxin Xuebao"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1007\/s00530-021-00801-w","article-title":"Hybrid features and semantic reinforcement network for image forgery detection","volume":"28","author":"Chen","year":"2022","journal-title":"Multimed. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Cao, G., Zhou, A., Huang, X., and Yang, L. (2020, January 6\u20139). Image tampering detection via semantic segmentation network. Proceedings of the 2020 15th IEEE International Conference on Signal Processing (ICSP), Beijing, China.","DOI":"10.1109\/ICSP48669.2020.9321086"},{"key":"ref_19","unstructured":"Yu, C. (2022). Research on Image Tampering Detection Based on Semantic Segmentation Network, East China Normal University."},{"key":"ref_20","unstructured":"Zhou, J. (2020). Research on Face Tampering Detection Based on Semantic Segmentation, University of Electronic Science and Technology."},{"key":"ref_21","first-page":"109","article-title":"Image forgery detection based on semantics","volume":"7","author":"Ke","year":"2014","journal-title":"Int. J. Hybrid Inf. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ye, K., Dong, J., Wang, W., Xu, J., and Tan, T. (2017, January 11\u201314). Image forgery detection based on semantic image understanding. Proceedings of the Computer Vision: Second CCF Chinese Conference, CCCV 2017, Tianjin, China. Proceedings, Part I.","DOI":"10.1007\/978-981-10-7299-4_39"},{"key":"ref_23","unstructured":"Zhi, T., Huang, W., He, T., He, P., and Qiao, Y. (2016, January 11\u201314). Detecting text in natural image with connectionist text proposal network. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2298","DOI":"10.1109\/TPAMI.2016.2646371","article-title":"An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition","volume":"39","author":"Shi","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"188560","DOI":"10.1109\/ACCESS.2024.3422318","article-title":"Multi-head Attention Based Bidirectional LSTM for Spelling Error Detection in the Indonesian Language","volume":"12","author":"Yanfi","year":"2024","journal-title":"IEEE Access"},{"key":"ref_26","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Nshish, G.A., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, Cornell University."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cui, Y., Che, W., Liu, T., Qin, B., Wang, S., and Hu, G. (2020). Revisiting pre-trained models for Chinese natural language processing. arXiv.","DOI":"10.18653\/v1\/2020.findings-emnlp.58"},{"key":"ref_28","first-page":"592","article-title":"A Method for Bilingual Tibetan-Chinese Scene Image Dataset Synthesis and Text Detection","volume":"34","author":"Hao","year":"2022","journal-title":"J. Comput.-Aided Des. Comput. Graph."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ch\u2019ng, C.K., and Chan, C.S. (2017, January 9\u201315). Total-text: A comprehensive dataset for scene text detection and recognition. Proceedings of the 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Kyoto, Japan.","DOI":"10.1109\/ICDAR.2017.157"},{"key":"ref_30","unstructured":"Yao, C., Bai, X., Liu, W., Ma, Y., and Tu, Z. (2012, January 16\u201321). Detecting texts of arbitrary orientations in natural images. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_31","unstructured":"(2021, December 21). Baidu Open Source OCR Introduction [EB\/OL]. Available online: https:\/\/gitee.com\/computer-vision\/PaddleOCR#\/."},{"key":"ref_32","unstructured":"(2024, November 30). CnOCR. Available online: https:\/\/github.com\/breezedeus\/cnocr."},{"key":"ref_33","unstructured":"Xu, L., Hu, H., Zhang, X., Li, L., Cao, C., Li, Y., Xu, Y., Sun, K., Yu, D., and Yu, C. (2004). CLUE: A Chinese language understanding evaluation benchmark. arXiv."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/4\/536\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:06:56Z","timestamp":1760029616000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/4\/536"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,31]]},"references-count":33,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["sym17040536"],"URL":"https:\/\/doi.org\/10.3390\/sym17040536","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,31]]}}}