{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:21:54Z","timestamp":1775578914848,"version":"3.50.1"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030893699","type":"print"},{"value":"9783030893705","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-89370-5_25","type":"book-chapter","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T01:02:59Z","timestamp":1635728579000},"page":"337-352","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Thinking in Patch: Towards Generalizable Forgery Detection with Patch Transformation"],"prefix":"10.1007","author":[{"given":"Xueqi","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Shuo","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Chenyu","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Min","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiaohan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Haiyong","family":"Xie","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,11,1]]},"reference":[{"key":"25_CR1","unstructured":"DeepFakes (2019). https:\/\/www.github.com\/deepfakes\/faceswap"},{"key":"25_CR2","unstructured":"FaceApp (2019). https:\/\/faceapp.com\/app"},{"key":"25_CR3","unstructured":"FaceSwap (2019). https:\/\/www.github.com\/MarekKowalski\/FaceSwap"},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Afchar, D., Nozick, V., Yamagishi, J., Echizen, I.: MesoNet: a compact facial video forgery detection network. In: 2018 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1\u20137 (2018)","DOI":"10.1109\/WIFS.2018.8630761"},{"issue":"7","key":"25_CR5","doi-asserted-by":"publisher","first-page":"3286","DOI":"10.1109\/TIP.2019.2895466","volume":"28","author":"JH Bappy","year":"2019","unstructured":"Bappy, J.H., Simons, C., Nataraj, L., Manjunath, B., Roy-Chowdhury, A.K.: Hybrid LSTM and encoder-decoder architecture for detection of image forgeries. IEEE Trans. Image Process. 28(7), 3286\u20133300 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"25_CR6","unstructured":"Berthelot, D., Schumm, T., Metz, L.: BEGAN: Boundary Equilibrium Generative Adversarial Networks. arXiv e-prints arXiv:1703.10717 (2017)"},{"key":"25_CR7","doi-asserted-by":"crossref","unstructured":"Bianchi, T., De Rosa, A., Piva, A.: Improved DCT coefficient analysis for forgery localization in jpeg images. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2444\u20132447 (2011)","DOI":"10.1109\/ICASSP.2011.5946978"},{"key":"25_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/978-3-030-58574-7_7","volume-title":"Computer Vision \u2013 ECCV 2020","author":"L Chai","year":"2020","unstructured":"Chai, L., Bau, D., Lim, S.-N., Isola, P.: What makes fake images detectable? Understanding properties that generalize. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12371, pp. 103\u2013120. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58574-7_7"},{"key":"25_CR9","doi-asserted-by":"crossref","unstructured":"Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J.: StarGAN: unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8789\u20138797, June 2018","DOI":"10.1109\/CVPR.2018.00916"},{"key":"25_CR10","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1251\u20131258, July 2017","DOI":"10.1109\/CVPR.2017.195"},{"key":"25_CR11","unstructured":"Cole, S.: AI-assisted fake porn is here and we\u2019re all fucked. Motherboard Tech by Vice, December 2017"},{"key":"25_CR12","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":"25_CR13","doi-asserted-by":"crossref","unstructured":"Dang, H., Liu, F., Stehouwer, J., Liu, X., Jain, A.K.: On the detection of digital face manipulation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5781\u20135790, June 2020","DOI":"10.1109\/CVPR42600.2020.00582"},{"key":"25_CR14","doi-asserted-by":"crossref","unstructured":"Du, M., Pentyala, S., Li, Y., Hu, X.: Towards generalizable deepfake detection with locality-aware autoencoder. In: Proceedings of the 29th ACM International Conference on Information and Knowledge Management, pp. 325\u2013334 (2020)","DOI":"10.1145\/3340531.3411892"},{"key":"25_CR15","unstructured":"Durall, R., Keuper, M., Pfreundt, F.J., Keuper, J.: Unmasking DeepFakes with simple Features. arXiv e-prints arXiv:1911.00686 (2019)"},{"key":"25_CR16","first-page":"2672","volume":"27","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27, 2672\u20132680 (2014)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"25_CR17","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 (CVPR). pp. 770\u2013778, June 2016","DOI":"10.1109\/CVPR.2016.90"},{"key":"25_CR18","unstructured":"Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. In: Proceedings of International Conference on Learning Representations (ICLR) (2018)"},{"key":"25_CR19","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4401\u20134410, June 2019","DOI":"10.1109\/CVPR.2019.00453"},{"key":"25_CR20","unstructured":"Kingma, D.P., Dhariwal, P.: Glow: generative flow with invertible 1 $$\\times $$ 1 convolutions. In: Advances in Neural Information Processing Systems NeurIPS 2018, pp. 10236\u201310245 (2018)"},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"Li, L., et al.: Face x-ray for more general face forgery detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5001\u20135010, June 2020","DOI":"10.1109\/CVPR42600.2020.00505"},{"key":"25_CR22","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 3730\u20133738, December 2015","DOI":"10.1109\/ICCV.2015.425"},{"issue":"5","key":"25_CR23","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.1109\/JSTSP.2020.3001516","volume":"14","author":"O Mayer","year":"2020","unstructured":"Mayer, O., Stamm, M.C.: Exposing fake images with forensic similarity graphs. IEEE J. Sel. Top. Sig. Process. 14(5), 1049\u20131064 (2020)","journal-title":"IEEE J. Sel. Top. Sig. Process."},{"key":"25_CR24","doi-asserted-by":"publisher","first-page":"1331","DOI":"10.1109\/TIFS.2019.2924552","volume":"15","author":"O Mayer","year":"2020","unstructured":"Mayer, O., Stamm, M.C.: Forensic similarity for digital images. IEEE Trans. Inf. Forensics Secur. 15, 1331\u20131346 (2020)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"25_CR25","doi-asserted-by":"crossref","unstructured":"Nataraj, L., et al.: Detecting GAN generated fake images using co-occurrence matrices. Electron. Imag. 2019(5), 532-1\u2013532-7 (2019)","DOI":"10.2352\/ISSN.2470-1173.2019.5.MWSF-532"},{"key":"25_CR26","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":"25_CR27","doi-asserted-by":"crossref","unstructured":"Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., Niessner, M.: Faceforensics++: Learning to detect manipulated facial images. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 1\u201311, October 2019","DOI":"10.1109\/ICCV.2019.00009"},{"issue":"4","key":"25_CR28","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":"25_CR29","doi-asserted-by":"crossref","unstructured":"Thies, J., Zollh\u00f6fer, M., Stamminger, M., Theobalt, C., Nie\u00dfner, M.: Face2face: real-time face capture and reenactment of RGB videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2387\u20132395, June 2016","DOI":"10.1109\/CVPR.2016.262"},{"key":"25_CR30","doi-asserted-by":"crossref","unstructured":"Wang, S.Y., Wang, O., Zhang, R., Owens, A., Efros, A.A.: CNN-generated images are surprisingly easy to spot... for now. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8695\u20138704, June 2020","DOI":"10.1109\/CVPR42600.2020.00872"},{"key":"25_CR31","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 (2019)","DOI":"10.1109\/WIFS47025.2019.9035107"},{"key":"25_CR32","doi-asserted-by":"crossref","unstructured":"Zhou, P., Han, X., Morariu, V.I., Davis, L.S.: Two-stream neural networks for tampered face detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1831\u20131839 (2017)","DOI":"10.1109\/CVPRW.2017.229"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2021: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-89370-5_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T01:15:34Z","timestamp":1635729334000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-89370-5_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030893699","9783030893705"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-89370-5_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 November 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hanoi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2021","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"382","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":"93","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":"28","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":"24% - 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","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":"5","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)"}}]}}