{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:55:20Z","timestamp":1783439720821,"version":"3.54.6"},"publisher-location":"Cham","reference-count":56,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031197802","type":"print"},{"value":"9783031197819","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\/978-3-031-19781-9_7","type":"book-chapter","created":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T12:12:59Z","timestamp":1666440779000},"page":"111-127","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["An Information Theoretic Approach for\u00a0Attention-Driven Face Forgery Detection"],"prefix":"10.1007","author":[{"given":"Ke","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taiping","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoshuai","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shen","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shouhong","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rongrong","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,23]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","unstructured":"Afchar, D., Nozick, V., Yamagishi, J., Echizen, I.: MesoNet: a compact facial video forgery detection network. In: WIFS, pp. 1\u20137. IEEE (2018)","DOI":"10.1109\/WIFS.2018.8630761"},{"key":"7_CR2","doi-asserted-by":"crossref","unstructured":"Agarwal, S., Farid, H.: Photo forensics from JPEG dimples. In: WIFS, pp. 1\u20136. IEEE (2017)","DOI":"10.1109\/WIFS.2017.8267641"},{"issue":"6","key":"7_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3130800.3130818","volume":"36","author":"H Averbuch-Elor","year":"2017","unstructured":"Averbuch-Elor, H., Cohen-Or, D., Kopf, J., Cohen, M.F.: Bringing portraits to life. ACM Trans. Graph. (TOG) 36(6), 1\u201313 (2017)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"7_CR4","unstructured":"Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096 (2018)"},{"key":"7_CR5","unstructured":"Bruce, N., Tsotsos, J.: Saliency based on information maximization. In: Advances in Neural Information Processing Systems, pp. 155\u2013162 (2005)"},{"issue":"9","key":"7_CR6","doi-asserted-by":"publisher","first-page":"950","DOI":"10.1167\/7.9.950","volume":"7","author":"N Bruce","year":"2007","unstructured":"Bruce, N., Tsotsos, J.: Attention based on information maximization. J. Vis. 7(9), 950\u2013950 (2007)","journal-title":"J. Vis."},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Cao, J., Ma, C., Yao, T., Chen, S., Ding, S., Yang, X.: End-to-end reconstruction-classification learning for face forgery detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4113\u20134122 (2022)","DOI":"10.1109\/CVPR52688.2022.00408"},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"Chen, M., Sedighi, V., Boroumand, M., Fridrich, J.: JPEG-phase-aware convolutional neural network for steganalysis of JPEG images. In: Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security, pp. 75\u201384 (2017)","DOI":"10.1145\/3082031.3083248"},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"Chen, S., Yao, T., Chen, Y., Ding, S., Li, J., Ji, R.: Local relation learning for face forgery detection. In: AAAI (2021)","DOI":"10.1609\/aaai.v35i2.16193"},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: CVPR, pp. 1251\u20131258 (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Cozzolino, D., Poggi, G., Verdoliva, L.: Recasting residual-based local descriptors as convolutional neural networks: an application to image forgery detection. In: Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security, pp. 159\u2013164 (2017)","DOI":"10.1145\/3082031.3083247"},{"key":"7_CR12","unstructured":"Dolhansky, B., et al.: The deepfake detection challenge dataset. arXiv preprint arXiv:2006.07397 (2020)"},{"issue":"3","key":"7_CR13","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."},{"issue":"8","key":"7_CR14","doi-asserted-by":"publisher","first-page":"2001","DOI":"10.1109\/TIFS.2018.2807791","volume":"13","author":"E Gonzalez-Sosa","year":"2018","unstructured":"Gonzalez-Sosa, E., Fierrez, J., Vera-Rodriguez, R., Alonso-Fernandez, F.: Facial soft biometrics for recognition in the wild: recent works, annotation, and cots evaluation. IEEE Trans. Inf. Forensics Secur. 13(8), 2001\u20132014 (2018)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"7_CR15","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: NeurlPS, pp. 2672\u20132680 (2014)"},{"key":"7_CR16","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: AAAI, vol. 36, pp. 735\u2013743 (2022)","DOI":"10.1609\/aaai.v36i1.19954"},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Gu, Z., et al.: Spatiotemporal inconsistency learning for deepfake video detection. In: ACM MM, pp. 3473\u20133481 (2021)","DOI":"10.1145\/3474085.3475508"},{"issue":"1","key":"7_CR18","first-page":"131","volume":"7","author":"TS Gunawan","year":"2017","unstructured":"Gunawan, T.S., Hanafiah, S.A.M., Kartiwi, M., Ismail, N., Za\u2019bah, N.F., Nordin, A.N.: Development of photo forensics algorithm by detecting photoshop manipulation using error level analysis. Indones. J. Electr. Eng. Comput. Sci. 7(1), 131\u2013137 (2017)","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"7_CR19","doi-asserted-by":"publisher","first-page":"103170","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":"7_CR20","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: CVPR, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"7_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1007\/978-3-642-33709-3_11","volume-title":"Computer Vision \u2013 ECCV 2012","author":"D Huang","year":"2012","unstructured":"Huang, D., De La Torre, F.: Facial action transfer with personalized bilinear regression. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7573, pp. 144\u2013158. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33709-3_11"},{"key":"7_CR22","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"7_CR23","doi-asserted-by":"crossref","unstructured":"Huang, Y., et al.: FakePolisher: making deepfakes more detection-evasive by shallow reconstruction. arXiv preprint arXiv:2006.07533 (2020)","DOI":"10.1145\/3394171.3413732"},{"key":"7_CR24","doi-asserted-by":"crossref","unstructured":"Juefei-Xu, F., Wang, R., Huang, Y., Guo, Q., Ma, L., Liu, Y.: Countering malicious deepfakes: survey, battleground, and horizon. arXiv preprint arXiv:2103.00218 (2021)","DOI":"10.1007\/s11263-022-01606-8"},{"issue":"4","key":"7_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3197517.3201283","volume":"37","author":"H Kim","year":"2018","unstructured":"Kim, H., et al.: Deep video portraits. ACM Trans. Graph. (TOG) 37(4), 1\u201314 (2018)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"7_CR26","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: CVPR, pp. 6458\u20136467 (2021)","DOI":"10.1109\/CVPR46437.2021.00639"},{"key":"7_CR27","doi-asserted-by":"crossref","unstructured":"Li, L., et al.: Face X-ray for more general face forgery detection. In: CVPR, pp. 5001\u20135010 (2020)","DOI":"10.1109\/CVPR42600.2020.00505"},{"key":"7_CR28","doi-asserted-by":"crossref","unstructured":"Li, X., et al.: Sharp multiple instance learning for deepfake video detection. In: ACM MM, pp. 1864\u20131872 (2020)","DOI":"10.1145\/3394171.3414034"},{"key":"7_CR29","unstructured":"Li, Y., Lyu, S.: Exposing deepfake videos by detecting face warping artifacts. arXiv preprint arXiv:1811.00656 (2018)"},{"key":"7_CR30","unstructured":"Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-DF: a new dataset for deepfake forensics. arXiv preprint arXiv:1909.12962 (2019)"},{"key":"7_CR31","doi-asserted-by":"crossref","unstructured":"Liu, H., et al.: Spatial-phase shallow learning: rethinking face forgery detection in frequency domain. In: CVPR, pp. 772\u2013781 (2021)","DOI":"10.1109\/CVPR46437.2021.00083"},{"key":"7_CR32","doi-asserted-by":"crossref","unstructured":"Liu, Z., Qi, X., Torr, P.H.: Global texture enhancement for fake face detection in the wild. In: CVPR, pp. 8060\u20138069 (2020)","DOI":"10.1109\/CVPR42600.2020.00808"},{"key":"7_CR33","doi-asserted-by":"crossref","unstructured":"Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317\u201316326 (2021)","DOI":"10.1109\/CVPR46437.2021.01605"},{"key":"7_CR34","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"},{"key":"7_CR35","doi-asserted-by":"crossref","unstructured":"Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83\u201392. IEEE (2019)","DOI":"10.1109\/WACVW.2019.00020"},{"key":"7_CR36","doi-asserted-by":"crossref","unstructured":"McCloskey, S., Albright, M.: Detecting GAN-generated imagery using color cues. arXiv preprint arXiv:1812.08247 (2018)","DOI":"10.1109\/ICIP.2019.8803661"},{"key":"7_CR37","doi-asserted-by":"crossref","unstructured":"Nguyen, H.H., Fang, F., Yamagishi, J., Echizen, I.: Multi-task learning for detecting and segmenting manipulated facial images and videos. arXiv preprint arXiv:1906.06876 (2019)","DOI":"10.1109\/BTAS46853.2019.9185974"},{"key":"7_CR38","doi-asserted-by":"crossref","unstructured":"Nguyen, H.H., Yamagishi, J., Echizen, I.: Capsule-forensics: using capsule networks to detect forged images and videos. In: ICASSP, pp. 2307\u20132311. IEEE (2019)","DOI":"10.1109\/ICASSP.2019.8682602"},{"key":"7_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1007\/978-3-030-58610-2_6","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Qian","year":"2020","unstructured":"Qian, Y., Yin, G., Sheng, L., Chen, Z., Shao, J.: Thinking in frequency: face forgery detection by mining frequency-aware clues. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12357, pp. 86\u2013103. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58610-2_6"},{"key":"7_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: ICCV, pp. 1\u201311 (2019)","DOI":"10.1109\/ICCV.2019.00009"},{"key":"7_CR41","doi-asserted-by":"crossref","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. In: ICCV, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"7_CR42","unstructured":"Shen, Z., Bello, I., Vemulapalli, R., Jia, X., Chen, C.H.: Global self-attention networks for image recognition. arXiv preprint arXiv:2010.03019 (2020)"},{"key":"7_CR43","unstructured":"Shi, B., Zhang, D., Dai, Q., Wang, J., Zhu, Z., Mu, Y.: Informative dropout for robust representation learning: a shape-bias perspective. In: ICML, vol. 1 (2020)"},{"key":"7_CR44","unstructured":"Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. In: CVPR (2019)"},{"key":"7_CR45","doi-asserted-by":"crossref","unstructured":"Sun, K., et al.: Domain general face forgery detection by learning to weight. In: AAAI, vol. 35, pp. 2638\u20132646 (2021)","DOI":"10.1609\/aaai.v35i3.16367"},{"key":"7_CR46","doi-asserted-by":"crossref","unstructured":"Sun, K., Yao, T., Chen, S., Ding, S., Li, J., Ji, R.: Dual contrastive learning for general face forgery detection. In: AAAI, vol. 36, pp. 2316\u20132324 (2022)","DOI":"10.1609\/aaai.v36i2.20130"},{"key":"7_CR47","unstructured":"Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. In: ICML (2019)"},{"issue":"4","key":"7_CR48","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 Trans. Graph. (TOG) 38(4), 1\u201312 (2019)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"7_CR49","doi-asserted-by":"crossref","unstructured":"Thies, J., Zollh\u00f6fer, M., Nie\u00dfner, M., Valgaerts, L., Stamminger, M., Theobalt, C.: Real-time expression transfer for facial reenactment. ACM Trans. Graph. 34(6), 183-1 (2015)","DOI":"10.1145\/2816795.2818056"},{"key":"7_CR50","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: CVPR, pp. 2387\u20132395 (2016)","DOI":"10.1109\/CVPR.2016.262"},{"key":"7_CR51","doi-asserted-by":"crossref","unstructured":"Wang, C., Deng, W.: Representative forgery mining for fake face detection. In: CVPR, pp. 14923\u201314932 (2021)","DOI":"10.1109\/CVPR46437.2021.01468"},{"key":"7_CR52","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: CVPR, pp. 7794\u20137803 (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"7_CR53","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1007\/978-3-030-63830-6_27","volume-title":"Neural Information Processing","author":"X Wang","year":"2020","unstructured":"Wang, X., Yao, T., Ding, S., Ma, L.: Face manipulation detection via auxiliary supervision. In: Yang, H., Pasupa, K., Leung, A.C.-S., Kwok, J.T., Chan, J.H., King, I. (eds.) ICONIP 2020. LNCS, vol. 12532, pp. 313\u2013324. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-63830-6_27"},{"key":"7_CR54","doi-asserted-by":"crossref","unstructured":"Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00222"},{"key":"7_CR55","doi-asserted-by":"crossref","unstructured":"Zhou, P., Han, X., Morariu, V.I., Davis, L.S.: Two-stream neural networks for tampered face detection. In: CVPRW, pp. 1831\u20131839. IEEE (2017)","DOI":"10.1109\/CVPRW.2017.229"},{"key":"7_CR56","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: ACM MM, pp. 2382\u20132390 (2020)","DOI":"10.1145\/3394171.3413769"}],"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\/978-3-031-19781-9_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:31:07Z","timestamp":1710261067000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19781-9_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031197802","9783031197819"],"references-count":56,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19781-9_7","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":"23 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)"}}]}}