{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T04:27:56Z","timestamp":1754108876113,"version":"3.37.3"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T00:00:00Z","timestamp":1690156800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T00:00:00Z","timestamp":1690156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004867","name":"Chongqing University of Technology","doi-asserted-by":"publisher","award":["gzlcx20223206"],"award-info":[{"award-number":["gzlcx20223206"]}],"id":[{"id":"10.13039\/501100004867","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61971078"],"award-info":[{"award-number":["61971078"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2024,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>With the rapid development of deep learning, face forgery detection methods have also achieved remarkable progress. However, most methods suffer significant performance degradation on low-quality compressed face images. It is due to: (a) The image artifacts will be blurred in the process of image compression, resulting in the model learning insufficient artifact traces; (b) Low-quality images will introduce a lot of noise information, and minimizing the training error causes the model to absorb all correlations in the training dataset recklessly, leading to the over-fitting problem. To solve the above problems, we consider learning domain invariant representations to inscribe the correct relevance, i.e., artifacts, to improve the robustness of low-quality images. Specifically, we propose a novel face forgery detector, called DIFLD. The model has the following components: (1) a high-frequency invariant feature learning module(hf-IFLM), which effectively retrieves the blurred artifacts in low-quality compressed images; and (2) a high-dimensional feature distribution learning module(hd-FDLM), that guides the network to learn more about the consistent features of distribution. With the above two modules, the whole framework can learn more discriminative correct artifact features in an end-to-end manner. Through extensive experiments, we show that our proposed method is more robust to image quality variations, especially in low-quality images. Our proposed method achieves a 3.67% improvement over the state-of-the-art methods on the challenging dataset NeuralTextures.<\/jats:p>","DOI":"10.1007\/s40747-023-01160-x","type":"journal-article","created":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T08:02:32Z","timestamp":1690185752000},"page":"357-368","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["DIFLD: domain invariant feature learning to detect low-quality compressed face forgery images"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9551-1855","authenticated-orcid":false,"given":"Yan","family":"Zou","sequence":"first","affiliation":[]},{"given":"Chaoyang","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Jianxun","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,24]]},"reference":[{"key":"1160_CR1","doi-asserted-by":"crossref","unstructured":"Sardar Alamgir, Umer Saiyed, Rout Ranjeet\u00a0Kumar (2023) Face recognition system using multicolor image analysis and template protection with biocryptosystem. In: Image and Vision Computing: 37th International Conference, IVCNZ 2022, Auckland, New Zealand, November 24\u201325, 2022, Revised Selected Papers, pp 457\u2013473. Springer","DOI":"10.1007\/978-3-031-25825-1_33"},{"key":"1160_CR2","doi-asserted-by":"crossref","unstructured":"Kathirvel A (2023) Debashreet Das, Stewart Kirubakaran, M\u00a0Subramaniam, and S\u00a0Naveneethan. Artificial intelligence\u2013based mobile bill payment system using biometric fingerprint. In: Recurrent Neural Networks, pp 233\u2013245. CRC Press","DOI":"10.1201\/9781003307822-16"},{"issue":"11","key":"1160_CR3","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Bing X, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139\u2013144","journal-title":"Commun ACM"},{"key":"1160_CR4","doi-asserted-by":"crossref","unstructured":"Lee Cheng-Han, Liu Ziwei, Wu Lingyun, Luo Ping (June 2020) Maskgan: Towards diverse and interactive facial image manipulation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR42600.2020.00559"},{"key":"1160_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107384","volume":"110","author":"L Gao","year":"2021","unstructured":"Gao L, Chen D, Zhao Z, Shao J, Shen HT (2021) Lightweight dynamic conditional gan with pyramid attention for text-to-image synthesis. Pattern Recogn 110:107384","journal-title":"Pattern Recogn"},{"key":"1160_CR6","unstructured":"Deepfakes. [Online]. Available: https:\/\/github.com\/deepfakes\/faceswap. Accessed 2021"},{"key":"1160_CR7","doi-asserted-by":"crossref","unstructured":"Zhao Cairong, Wang Chutian, Hu Guosheng, Chen Haonan, Liu Chun, Tang Jinhui (2023) Istvt: Interpretable spatial-temporal video transformer for deepfake detection. IEEE Transactions on Information Forensics and Security","DOI":"10.1109\/TIFS.2023.3239223"},{"key":"1160_CR8","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neunet.2022.11.031","volume":"159","author":"B Liang","year":"2023","unstructured":"Liang B, Wang Z, Huang B, Zou Q, Wang Q, Liang J (2023) Depth map guided triplet network for deepfake face detection. Neural Netw 159:34\u201342","journal-title":"Neural Netw"},{"key":"1160_CR9","doi-asserted-by":"crossref","unstructured":"Mohiuddin Sk, Sheikh Khalid\u00a0Hassan, Malakar Samir, Vel\u00e1squez Juan\u00a0D, Sarkar Ram (2023) A hierarchical feature selection strategy for deepfake video detection. Neural Computing and Applications, pp 1\u201318","DOI":"10.1007\/s00521-023-08201-z"},{"key":"1160_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2020.107616","volume":"174","author":"H Li","year":"2020","unstructured":"Li H, Li B, Tan S, Huang J (2020) Identification of deep network generated images using disparities in color components. Signal Process 174:107616","journal-title":"Signal Process"},{"key":"1160_CR11","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1016\/j.ins.2022.06.003","volume":"607","author":"Z Xia","year":"2022","unstructured":"Xia Z, Qiao T, Ming X, Zheng N, Xie S (2022) Towards deepfake video forensics based on facial textural disparities in multi-color channels. Inf Sci 607:654\u2013669","journal-title":"Inf Sci"},{"key":"1160_CR12","doi-asserted-by":"crossref","unstructured":"Ciftci Umur\u00a0Aybars, Demir Ilke, Yin Lijun (2020) Fakecatcher: Detection of synthetic portrait videos using biological signals. IEEE transactions on pattern analysis and machine intelligence","DOI":"10.1109\/TPAMI.2020.3009287"},{"key":"1160_CR13","doi-asserted-by":"crossref","unstructured":"Guo Hui, Hu Shu, Wang Xin, Chang Ming-Ching, Lyu Siwei (2022) Eyes tell all: Irregular pupil shapes reveal gan-generated faces. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 2904\u20132908. IEEE","DOI":"10.1109\/ICASSP43922.2022.9746597"},{"key":"1160_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118423","volume":"210","author":"S Ganguly","year":"2022","unstructured":"Ganguly S, Ganguly A, Mohiuddin S, Malakar S, Sarkar R (2022) Vixnet: Vision transformer with xception network for deepfakes based video and image forgery detection. Expert Syst Appl 210:118423","journal-title":"Expert Syst Appl"},{"key":"1160_CR15","doi-asserted-by":"crossref","unstructured":"Cao Junyi, Ma Chao, Yao Taiping, Chen Shen, Ding Shouhong, Yang Xiaokang (2022). End-to-end reconstruction-classification learning for face forgery detection. 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 4103\u20134112","DOI":"10.1109\/CVPR52688.2022.00408"},{"key":"1160_CR16","doi-asserted-by":"crossref","unstructured":"Hsu Chih-Chung, Lee Chia-Yen, Zhuang Yi-Xiu (2018) Learning to detect fake face images in the wild. In: 2018 international symposium on computer, consumer and control (IS3C), pp 388\u2013391. IEEE","DOI":"10.1109\/IS3C.2018.00104"},{"key":"1160_CR17","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Commun ACM 60:84\u201390","journal-title":"Commun ACM"},{"key":"1160_CR18","doi-asserted-by":"crossref","unstructured":"Wang Chengrui, Deng Weihong (2021) Representative forgery mining for fake face detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 14923\u201314932","DOI":"10.1109\/CVPR46437.2021.01468"},{"key":"1160_CR19","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.ins.2022.04.014","volume":"601","author":"B Chen","year":"2022","unstructured":"Chen B, Li T, Ding W (2022) Detecting deepfake videos based on spatiotemporal attention and convolutional lstm. Inf Sci 601:58\u201370","journal-title":"Inf Sci"},{"key":"1160_CR20","doi-asserted-by":"crossref","unstructured":"Dong Xiaoyi, Bao Jianmin, Chen Dongdong, Zhang Ting, Zhang Weiming, Yu Nenghai, Chen Dong, Wen Fang, Guo Baining (2022) Protecting celebrities from deepfake with identity consistency transformer. 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 9458\u20139468","DOI":"10.1109\/CVPR52688.2022.00925"},{"key":"1160_CR21","doi-asserted-by":"crossref","unstructured":"Zhao Hanqing, Zhou Wenbo, Chen Dongdong, Wei Tianyi, Zhang Weiming, Yu Nenghai (2021) Multi-attentional deepfake detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2185\u20132194","DOI":"10.1109\/CVPR46437.2021.00222"},{"key":"1160_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109179","volume":"135","author":"H Chen","year":"2023","unstructured":"Chen H, Li Y, Lin D, Li B, Junqiang W (2023) Watching the big artifacts: Exposing deepfake videos via bi-granularity artifacts. Pattern Recogn 135:109179","journal-title":"Pattern Recogn"},{"key":"1160_CR23","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1609\/aaai.v36i1.19886","volume":"36","author":"S Woo","year":"2022","unstructured":"Woo S et al (2022) Add: Frequency attention and multi-view based knowledge distillation to detect low-quality compressed deepfake images. In Proceedings of the AAAI Conference on Artificial Intelligence 36:122\u2013130","journal-title":"In Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"1160_CR24","unstructured":"Arjovsky Martin, Bottou L\u00e9on, Gulrajani Ishaan, Lopez-Paz David (2019) Invariant risk minimization. arXiv preprint arXiv:1907.02893"},{"key":"1160_CR25","doi-asserted-by":"crossref","unstructured":"R\u00f6ssler Andreas, Cozzolino Davide, Verdoliva Luisa, Riess Christian, Thies Justus, Nie\u00dfner Matthias (2019) Faceforensics++: Learning to detect manipulated facial images. 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp 1\u201311","DOI":"10.1109\/ICCV.2019.00009"},{"key":"1160_CR26","doi-asserted-by":"crossref","unstructured":"Li Yuezun, Chang Ming-Ching, Lyu Siwei (2018) In ictu oculi: Exposing ai created fake videos by detecting eye blinking. In: 2018 IEEE international workshop on information forensics and security (WIFS), pp 1\u20137. IEEE","DOI":"10.1109\/WIFS.2018.8630787"},{"key":"1160_CR27","doi-asserted-by":"crossref","unstructured":"McCloskey Scott, Albright Michael (2019) Detecting gan-generated imagery using saturation cues. In: 2019 IEEE international conference on image processing (ICIP), pp 4584\u20134588. IEEE","DOI":"10.1109\/ICIP.2019.8803661"},{"key":"1160_CR28","doi-asserted-by":"crossref","unstructured":"Wang G, Jiang Q, Jin X, Cui X (2022) Ffr_fd: Effective and fast detection of deepfakes via feature point defects. Inf Sci 596:472\u2013488","DOI":"10.1016\/j.ins.2022.03.026"},{"key":"1160_CR29","doi-asserted-by":"crossref","unstructured":"Wang Gaojian, Jiang Qian, Jin Xin, Li Wei, Cui Xiaohui (2022) Mc-lcr: Multimodal contrastive classification by locally correlated representations for effective face forgery detection. Knowledge-Based Systems, p 109114","DOI":"10.1016\/j.knosys.2022.109114"},{"key":"1160_CR30","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/9780262170055.001.0001","volume-title":"Dataset shift in machine learning","author":"J Quinonero-Candela","year":"2008","unstructured":"Quinonero-Candela J, Sugiyama M, Schwaighofer A, Lawrence ND (2008) Dataset shift in machine learning. MIT Press, Cambridge"},{"issue":"1","key":"1160_CR31","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1175\/1520-0477(1998)079<0061:APGTWA>2.0.CO;2","volume":"79","author":"C Torrence","year":"1998","unstructured":"Torrence C, Compo GP (1998) A practical guide to wavelet analysis. Bull Am Meteor Soc 79(1):61\u201378","journal-title":"Bull Am Meteor Soc"},{"issue":"4","key":"1160_CR32","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1109\/5.488702","volume":"84","author":"S Mallat","year":"1996","unstructured":"Mallat S (1996) Wavelets for a vision. Proc IEEE 84(4):604\u2013614","journal-title":"Proc IEEE"},{"key":"1160_CR33","unstructured":"Bracewell Ronald\u00a0Newbold, Bracewell Ronald\u00a0N (1986) The Fourier transform and its applications, volume 31999. McGraw-Hill New York"},{"issue":"12","key":"1160_CR34","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1109\/MSPEC.1967.5217220","volume":"4","author":"E Oran Brigham","year":"1967","unstructured":"Oran Brigham E, Morrow RE (1967) The fast fourier transform. IEEE Spectr 4(12):63\u201370","journal-title":"IEEE Spectr"},{"key":"1160_CR35","doi-asserted-by":"crossref","unstructured":"He Kaiming, Zhang Xiangyu, Ren Shaoqing, Sun Jian (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1160_CR36","doi-asserted-by":"crossref","unstructured":"Zhang Xu, Karaman Svebor, Chang Shih-Fu (2019) Detecting and simulating artifacts in gan fake images. In: 2019 IEEE international workshop on information forensics and security (WIFS), pp 1\u20136. IEEE","DOI":"10.1109\/WIFS47025.2019.9035107"},{"key":"1160_CR37","doi-asserted-by":"crossref","unstructured":"Hu Hailong, Li Yantao, Zhu Zhangqian, Zhou Gang (2018) Cnnauth: continuous authentication via two-stream convolutional neural networks. In: 2018 IEEE international conference on networking, architecture and storage (NAS), pp 1\u20139. IEEE","DOI":"10.1109\/NAS.2018.8515693"},{"issue":"3","key":"1160_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3397179","volume":"16","author":"Y Li","year":"2020","unstructured":"Li Y, Hailong H, Zhu Z, Zhou G (2020) Scanet: sensor-based continuous authentication with two-stream convolutional neural networks. ACM Transactions on Sensor Networks (TOSN) 16(3):1\u201327","journal-title":"ACM Transactions on Sensor Networks (TOSN)"},{"key":"1160_CR39","doi-asserted-by":"publisher","first-page":"18461","DOI":"10.1007\/s11042-020-10420-8","volume":"80","author":"A Kohli","year":"2021","unstructured":"Kohli A, Gupta A (2021) Detecting deepfake, faceswap and face2face facial forgeries using frequency cnn. Multimedia Tools and Applications 80:18461\u201318478","journal-title":"Multimedia Tools and Applications"},{"key":"1160_CR40","doi-asserted-by":"crossref","unstructured":"Huang Yuge, Shen Pengcheng, Tai Ying, Li Shaoxin, Liu Xiaoming, Li Jilin, Huang Feiyue, Ji Rongrong (2020) Improving face recognition from hard samples via distribution distillation loss. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXX 16, pp 138\u2013154. Springer","DOI":"10.1007\/978-3-030-58577-8_9"},{"key":"1160_CR41","unstructured":"Van\u00a0der Maaten Laurens, Hinton Geoffrey (2008) Visualizing data using t-sne. Journal of machine learning research, 9(11)"},{"issue":"8","key":"1160_CR42","first-page":"49","volume":"43","author":"Y SHONG","year":"2017","unstructured":"SHONG Y, GAO X, ZHANG D et al (2017) The piecewise non-linear approximation of the sigmoid function and its implementation in fpga. Application of Electronic Technique 43(8):49\u201351","journal-title":"Application of Electronic Technique"},{"issue":"3","key":"1160_CR43","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/0146-664X(80)90054-4","volume":"14","author":"P-E Danielsson","year":"1980","unstructured":"Danielsson P-E (1980) Euclidean distance mapping. Comput Graphics Image Process 14(3):227\u2013248","journal-title":"Comput Graphics Image Process"},{"key":"1160_CR44","doi-asserted-by":"crossref","unstructured":"Zhang Ying, Xiang Tao, Hospedales Timothy\u00a0M, Lu Huchuan (2018) Deep mutual learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4320\u20134328","DOI":"10.1109\/CVPR.2018.00454"},{"key":"1160_CR45","doi-asserted-by":"crossref","unstructured":"Thies Justus, Zollh\u00f6fer Michael, Stamminger Marc, Theobalt Christian, Nie\u00dfner Matthias (2016) Face2face: Real-time face capture and reenactment of rgb videos. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2387\u20132395","DOI":"10.1109\/CVPR.2016.262"},{"key":"1160_CR46","unstructured":"Kowalskil Marek Faceswap. [Online]. Available: https:\/\/github.com\/MarekKowalski\/FaceSwap\/. Accessed 2020"},{"key":"1160_CR47","doi-asserted-by":"crossref","unstructured":"Thies Justus, Zollh\u00f6fer Michael, Nie\u00dfner Matthias (2019) Deferred neural rendering: Image synthesis using neural textures. arxiv Computer Vision and Pattern Recognition","DOI":"10.1145\/3306346.3323035"},{"issue":"10","key":"1160_CR48","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","volume":"23","author":"K Zhang","year":"2016","unstructured":"Zhang K, Zhang Z, Li Z, Qiao Yu (2016) Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process Lett 23(10):1499\u20131503","journal-title":"IEEE Signal Process Lett"},{"key":"1160_CR49","unstructured":"Paszke Adam, Gross Sam, Massa Francisco, Lerer Adam, Bradbury James, Chanan Gregory, Killeen Trevor, Lin Zeming, Gimelshein Natalia, Antiga Luca, et\u00a0al (2019) Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32"},{"key":"1160_CR50","unstructured":"Kingma Diederik\u00a0P, Ba Jimmy (2014) Adam: A method for stochastic optimization. CoRR, arXiv:1412.6980"},{"key":"1160_CR51","unstructured":"Dogonadze Nika, Obernosterer Jana, Hou Ji (2020) Deep face forgery detection. arXiv preprint arXiv:2004.11804"},{"key":"1160_CR52","doi-asserted-by":"crossref","unstructured":"Qian Yuyang, Yin Guojun, Sheng Lu, Chen Zixuan, Shao Jing (2020) Thinking in frequency: Face forgery detection by mining frequency-aware clues. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XII, pp 86\u2013103. Springer","DOI":"10.1007\/978-3-030-58610-2_6"},{"key":"1160_CR53","unstructured":"Romero Adriana, Ballas Nicolas, Kahou Samira\u00a0Ebrahimi, Chassang Antoine, Gatta Carlo, Bengio Yoshua (2014) Fitnets: Hints for thin deep nets. arXiv preprint arXiv:1412.6550"},{"key":"1160_CR54","unstructured":"Zagoruyko Sergey, Komodakis Nikos (2016) Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. arXiv preprint arXiv:1612.03928"},{"key":"1160_CR55","doi-asserted-by":"crossref","unstructured":"Wang Xiaolong, Girshick Ross, Gupta Abhinav, He Kaiming (2018) Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7794\u20137803","DOI":"10.1109\/CVPR.2018.00813"},{"key":"1160_CR56","doi-asserted-by":"crossref","unstructured":"Das Sowmen, Seferbekov Selim, Datta Arup, Islam Md, Amin Md, et\u00a0al (2021) Towards solving the deepfake problem: An analysis on improving deepfake detection using dynamic face augmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 3776\u20133785","DOI":"10.1109\/ICCVW54120.2021.00421"},{"key":"1160_CR57","doi-asserted-by":"crossref","unstructured":"Xu Ying, Raja Kiran, Verdoliva Luisa, Pedersen Marius (2023) Learning pairwise interaction for generalizable deepfake detection. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp 672\u2013682","DOI":"10.1109\/WACVW58289.2023.00074"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01160-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-023-01160-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01160-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T22:17:34Z","timestamp":1707603454000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-023-01160-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,24]]},"references-count":57,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["1160"],"URL":"https:\/\/doi.org\/10.1007\/s40747-023-01160-x","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"type":"print","value":"2199-4536"},{"type":"electronic","value":"2198-6053"}],"subject":[],"published":{"date-parts":[[2023,7,24]]},"assertion":[{"value":"6 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}