{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T21:24:52Z","timestamp":1769635492060,"version":"3.49.0"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031198144","type":"print"},{"value":"9783031198151","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-19815-1_13","type":"book-chapter","created":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T23:11:54Z","timestamp":1666221114000},"page":"215-232","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Detecting Tampered Scene Text in the Wild"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0228-6220","authenticated-orcid":false,"given":"Yuxin","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6249-5315","authenticated-orcid":false,"given":"Hongtao","family":"Xie","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7285-6671","authenticated-orcid":false,"given":"Mengting","family":"Xing","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4567-3869","authenticated-orcid":false,"given":"Jing","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3254-0058","authenticated-orcid":false,"given":"Shenggao","family":"Zhu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1151-1792","authenticated-orcid":false,"given":"Yongdong","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,20]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"Bibi, M., Hamid, A., Moetesum, M., Siddiqi, I.: Document forgery detection using printer source identification-a text-independent approach. In: 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), vol. 8, pp. 7\u201312. IEEE (2019)","DOI":"10.1109\/ICDARW.2019.70134"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Du, Y., et al.: SVTR: scene text recognition with a single visual model. In: IJCAI (2022)","DOI":"10.24963\/ijcai.2022\/124"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Fang, S., Xie, H., Wang, Y., Mao, Z., Zhang, Y.: Read like humans: autonomous, bidirectional and iterative language modeling for scene text recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7098\u20137107 (2021)","DOI":"10.1109\/CVPR46437.2021.00702"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Ge, J., Xie, H., Min, S., Zhang, Y.: Semantic-guided reinforced region embedding for generalized zero-shot learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 1406\u20131414 (2021)","DOI":"10.1609\/aaai.v35i2.16230"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Gupta, A., Vedaldi, A., Zisserman, A.: Synthetic data for text localisation in natural images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2315\u20132324 (2016)","DOI":"10.1109\/CVPR.2016.254"},{"key":"13_CR6","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":"13_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask r-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"13_CR9","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 (2021)","DOI":"10.24963\/ijcai.2021\/102"},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Karatzas, D., et al.: ICDAR 2013 robust reading competition. In: 2013 12th International Conference on Document Analysis and Recognition, pp. 1484\u20131493. IEEE (2013)","DOI":"10.1109\/ICDAR.2013.221"},{"key":"13_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1007\/978-3-030-41404-7_10","volume-title":"Pattern Recognition","author":"S Kundu","year":"2020","unstructured":"Kundu, S., Shivakumara, P., Grouver, A., Pal, U., Lu, T., Blumenstein, M.: A new forged handwriting detection method based on fourier spectral density and variation. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W.Q. (eds.) ACPR 2019. LNCS, vol. 12046, pp. 136\u2013150. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-41404-7_10"},{"key":"13_CR12","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":"13_CR13","doi-asserted-by":"crossref","unstructured":"Li, P., Li, Y., Xie, H., Zhang, L.: Neighborhood-adaptive structure augmented metric learning. In: AAAI (2022)","DOI":"10.1609\/aaai.v36i2.20025"},{"issue":"8","key":"13_CR14","doi-asserted-by":"publisher","first-page":"3676","DOI":"10.1109\/TIP.2018.2825107","volume":"27","author":"M Liao","year":"2018","unstructured":"Liao, M., Shi, B., Bai, X.: Textboxes++: a single-shot oriented scene text detector. IEEE Trans. Image Process. 27(8), 3676\u20133690 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Liao, M., Shi, B., Bai, X., Wang, X., Liu, W.: Textboxes: a fast text detector with a single deep neural network. In: Thirty-first AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.11196"},{"key":"13_CR16","doi-asserted-by":"crossref","unstructured":"Liu, Y., Chen, H., Shen, C., He, T., Jin, L., Wang, L.: Abcnet: real-time scene text spotting with adaptive bezier-curve network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9809\u20139818 (2020)","DOI":"10.1109\/CVPR42600.2020.00983"},{"key":"13_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1007\/978-3-030-58571-6_39","volume-title":"Computer Vision \u2013 ECCV 2020","author":"I Masi","year":"2020","unstructured":"Masi, I., Killekar, A., Mascarenhas, R.M., Gurudatt, S.P., AbdAlmageed, W.: Two-branch recurrent network for isolating deepfakes in videos. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12352, pp. 667\u2013684. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58571-6_39"},{"issue":"17","key":"13_CR18","doi-asserted-by":"publisher","first-page":"4744","DOI":"10.1049\/iet-ipr.2020.0590","volume":"14","author":"L Nandanwar","year":"2020","unstructured":"Nandanwar, L., et al.: Forged text detection in video, scene, and document images. IET Image Process. 14(17), 4744\u20134755 (2020)","journal-title":"IET Image Process."},{"key":"13_CR19","doi-asserted-by":"crossref","unstructured":"Qiao, L., et al.: Mango: a mask attention guided one-stage scene text spotter. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2467\u20132476 (2021)","DOI":"10.1609\/aaai.v35i3.16348"},{"key":"13_CR20","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. 28 (2015)"},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Roy, P., Bhattacharya, S., Ghosh, S., Pal, U.: STEFANN: scene text editor using font adaptive neural network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13228\u201313237 (2020)","DOI":"10.1109\/CVPR42600.2020.01324"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"Sheng, F., Chen, Z., Xu, B.: NRTR: a no-recurrence sequence-to-sequence model for scene text recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 781\u2013786. IEEE (2019)","DOI":"10.1109\/ICDAR.2019.00130"},{"key":"13_CR23","unstructured":"Sheng, T., Chen, J., Lian, Z.: Centripetaltext: an efficient text instance representation for scene text detection. Adv. Neural Inf. Process. Syst. 34, 335\u2013346 (2021)"},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"da Silva Barbosa, R., Lins, R.D., De Lira, E.D.F., Camara, A.C.A.: Later added strokes or text-fraud detection in documents written with ballpoint pens. In: 2014 14th International Conference on Frontiers in Handwriting Recognition, pp. 517\u2013522. IEEE (2014)","DOI":"10.1109\/ICFHR.2014.93"},{"key":"13_CR25","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"13_CR26","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shu, M., Lyu, P., Li, R., Zhou, C., Shen, X., Jia, J.: Learning shape-aware embedding for scene text detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4234\u20134243 (2019)","DOI":"10.1109\/CVPR.2019.00436"},{"key":"13_CR27","doi-asserted-by":"crossref","unstructured":"Wang, P., et al.: A single-shot arbitrarily-shaped text detector based on context attended multi-task learning. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 1277\u20131285 (2019)","DOI":"10.1145\/3343031.3350988"},{"key":"13_CR28","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, E., Li, X., Hou, W., Lu, T., Yu, G., Shao, S.: Shape robust text detection with progressive scale expansion network. In: CVPR, pp. 9336\u20139345 (2019)","DOI":"10.1109\/CVPR.2019.00956"},{"key":"13_CR29","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: Efficient and accurate arbitrary-shaped text detection with pixel aggregation network. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8440\u20138449 (2019)","DOI":"10.1109\/ICCV.2019.00853"},{"key":"13_CR30","doi-asserted-by":"crossref","unstructured":"Wang, X., Jiang, Y., Luo, Z., Liu, C.L., Choi, H., Kim, S.: Arbitrary shape scene text detection with adaptive text region representation. In: CVPR, pp. 6449\u20136458 (2019)","DOI":"10.1109\/CVPR.2019.00661"},{"key":"13_CR31","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xie, H., Fang, S., Wang, J., Zhu, S., Zhang, Y.: From two to one: a new scene text recognizer with visual language modeling network. In: ICCV, pp. 14194\u201314203 (2021)","DOI":"10.1109\/ICCV48922.2021.01393"},{"key":"13_CR32","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xie, H., Fu, Z., Zhang, Y.: DSRN: a deep scale relationship network for scene text detection. In: IJCAI, pp. 947\u2013953 (2019)","DOI":"10.24963\/ijcai.2019\/133"},{"key":"13_CR33","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xie, H., Zha, Z.J., Xing, M., Fu, Z., Zhang, Y.: Contournet: taking a further step toward accurate arbitrary-shaped scene text detection. In: CVPR, pp. 11753\u201311762 (2020)","DOI":"10.1109\/CVPR42600.2020.01177"},{"key":"13_CR34","doi-asserted-by":"crossref","unstructured":"Wu, L., Zhang, C., Liu, J., Han, J., Liu, J., Ding, E., Bai, X.: Editing text in the wild. In: ACM MM, pp. 1500\u20131508 (2019)","DOI":"10.1145\/3343031.3350929"},{"key":"13_CR35","doi-asserted-by":"crossref","unstructured":"Xie, E., Zang, Y., Shao, S., Yu, G., Yao, C., Li, G.: Scene text detection with supervised pyramid context network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 9038\u20139045 (2019)","DOI":"10.1609\/aaai.v33i01.33019038"},{"key":"13_CR36","doi-asserted-by":"crossref","unstructured":"Xing, M., et al.: Boundary-aware arbitrary-shaped scene text detector with learnable embedding network. IEEE Trans. Multimedia 24, 3129\u20133143 (2021)","DOI":"10.1109\/TMM.2021.3093727"},{"key":"13_CR37","doi-asserted-by":"crossref","unstructured":"Xue, C., Lu, S., Zhang, W.: Msr: multi-scale shape regression for scene text detection. arXiv preprint arXiv:1901.02596 (2019)","DOI":"10.24963\/ijcai.2019\/139"},{"key":"13_CR38","doi-asserted-by":"crossref","unstructured":"Yang, Q., Huang, J., Lin, W.: Swaptext: image based texts transfer in scenes. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14700\u201314709 (2020)","DOI":"10.1109\/CVPR42600.2020.01471"},{"key":"13_CR39","unstructured":"Zaremba, W., Sutskever, I., Vinyals, O.: Recurrent neural network regularization. arXiv preprint arXiv:1409.2329 (2014)"},{"key":"13_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, C., Liang, B., Huang, E.A.: Look more than once: an accurate detector for text of arbitrary shapes. In: CVPR, pp. 10552\u201310561 (2019)","DOI":"10.1109\/CVPR.2019.01080"},{"key":"13_CR41","doi-asserted-by":"crossref","unstructured":"Zhang, X., Karaman, S., Chang, S.F.: Detecting and simulating artifacts in GAN fake images. In: 2019 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1\u20136. IEEE (2019)","DOI":"10.1109\/WIFS47025.2019.9035107"},{"key":"13_CR42","unstructured":"Zheng, T., Chen, Z., Fang, S., Xie, H., Jiang, Y.G.: Cdistnet: Perceiving multi-domain character distance for robust text recognition. arXiv preprint arXiv:2111.11011 (2021)"},{"key":"13_CR43","doi-asserted-by":"crossref","unstructured":"Zhou, X., et al.: East: an efficient and accurate scene text detector. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 5551\u20135560 (2017)","DOI":"10.1109\/CVPR.2017.283"},{"key":"13_CR44","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Chen, J., Liang, L., Kuang, Z., Jin, L., Zhang, W.: Fourier contour embedding for arbitrary-shaped text detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3123\u20133131 (2021)","DOI":"10.1109\/CVPR46437.2021.00314"},{"key":"13_CR45","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Du, J.: Sliding line point regression for shape robust scene text detection. In: ICPR, pp. 3735\u20133740. IEEE (2018)","DOI":"10.1109\/ICPR.2018.8545067"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19815-1_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T23:24:20Z","timestamp":1666394660000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19815-1_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031198144","9783031198151"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19815-1_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"20 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1645","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.21","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.91","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}