{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T04:39:43Z","timestamp":1782275983721,"version":"3.54.5"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T00:00:00Z","timestamp":1751932800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T00:00:00Z","timestamp":1751932800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Sinopec Postdoctoral Research Fund","award":["YKB2411"],"award-info":[{"award-number":["YKB2411"]}]},{"name":"National Natural Science Foundation of China under Grant","award":["62173345"],"award-info":[{"award-number":["62173345"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-025-00892-7","type":"journal-article","created":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T06:20:56Z","timestamp":1751955656000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Hybrid Domain Perception Combined With Multi-expert Decoding to Improve Image Forgery Localization"],"prefix":"10.1007","volume":"18","author":[{"given":"Xuchao","family":"Gong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongjie","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peiying","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaohui","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,8]]},"reference":[{"key":"892_CR1","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models[J]. arXiv:2006.11239, pp. 1\u20135 (2020)"},{"key":"892_CR2","unstructured":"Ramesh, A., Dhariwal, P., Nichol, A., et al.: Hierarchical text-conditional image generation with CLIP latents. arXiv:2204.06125, pp. 1\u201327 (2022)"},{"key":"892_CR3","unstructured":"Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. In: Proceedings of the International Conference on Learning Representations. Los Alamitos: IEEE Computer Society Press, pp. 1\u201335 (2019)"},{"key":"892_CR4","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T. et al.: A style-based generator architecture for generative adversarial networks.In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, pp. 4401\u20134410 (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"892_CR5","unstructured":"Kingma, D.P.: Max welling. Auto-encoding variational Bayes. arXiv:1312.6114, pp. 1\u201314 (2013)"},{"key":"892_CR6","unstructured":"Vahdat, A., Kautz, J.: NVAE: a deep hierarchical variational autoencoder. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. Los Alamitos: IEEE Computer Society Press, pp. 19667-19679 (2020)"},{"issue":"3","key":"892_CR7","doi-asserted-by":"publisher","first-page":"345","DOI":"10.11591\/eei.v7i3.754","volume":"7","author":"A Gupta","year":"2018","unstructured":"Gupta, A.: A new copy move forgery detection technique using adaptive over-segmentation and feature point matching. Bull. Electr. Eng. Inform. 7(3), 345\u2013349 (2018)","journal-title":"Bull. Electr. Eng. Inform."},{"key":"892_CR8","unstructured":"Kniaz, V.V., Knyaz, V., Remondino, F., et al. The point where reality meets fantasy: mixed adversarial generators for image splice detection. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems. Los Alamitos: IEEE Computer Society Press, pp. 215\u2013226 (2019)"},{"issue":"11","key":"892_CR9","doi-asserted-by":"publisher","first-page":"2284","DOI":"10.1109\/TIFS.2015.2455334","volume":"10","author":"D Cozzolino","year":"2015","unstructured":"Cozzolino, D., Poggi, G., Verdoliva, L., et al.: Efficient dense-field copy-move forgery detection. IEEE Trans. Inf. Forensics Secur. 10(11), 2284\u20132297 (2015)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"892_CR10","doi-asserted-by":"crossref","unstructured":"Wu, Y., Abd-Almageed, W., Natarajan, P.: Busternet: Detecting copy-move image forgery with source\/target localization. In: Proceedings of the Computer Vision-ECCV 2018 the 15th European Conference. Los Alamitos: IEEE Computer Society Press, pp. 170\u2013186 (2018)","DOI":"10.1007\/978-3-030-01231-1_11"},{"key":"892_CR11","doi-asserted-by":"crossref","unstructured":"Wu, Y., Abd-Almageed, W., Natarajan, P.: Image copy-move forgery detection via an end-to-end deep neural network. In: Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision. Los Alamitos: IEEE Computer Society Press, pp. 1907\u20131915 (2018)","DOI":"10.1109\/WACV.2018.00211"},{"issue":"11","key":"892_CR12","doi-asserted-by":"publisher","first-page":"2691","DOI":"10.1109\/TIFS.2018.2825953","volume":"13","author":"B Bayar","year":"2018","unstructured":"Bayar, B., Stamm, M.C.: Constrained convolutional neural networks: A new approach towards general purpose image manipulation detection[J]. IEEE Trans. Inf. Forensics Secur. 13(11), 2691\u20132706 (2018)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"892_CR13","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.jvcir.2018.01.010","volume":"51","author":"R Salloum","year":"2018","unstructured":"Salloum, R., Ren, Y., Jay Kuo, C.-C.: Image splicing localization using a multi-task fully convolutional network. J. Vis. Commun. Image Represent. 51, 201\u2013209 (2018)","journal-title":"J. Vis. Commun. Image Represent."},{"issue":"3","key":"892_CR14","first-page":"9351","volume":"3","author":"SG Rasse","year":"2014","unstructured":"Rasse, S.G.: Review of detection of digital image splicing forgeries with illumination color estimation. Comput. Sci. 3(3), 9351\u20139359 (2014)","journal-title":"Comput. Sci."},{"key":"892_CR15","doi-asserted-by":"crossref","unstructured":"Wu, Y., Abd-Almageed, W., Natarajan, P.: Deep matching and validation network: an end-to-end solution to constrained image splicing localization and detection. In: Proceedings of the 25th ACM International Conference on Multimedia. Los Alamitos: IEEE Computer Society Press, pp. 1480\u20131502 (2017)","DOI":"10.1145\/3123266.3123411"},{"key":"892_CR16","doi-asserted-by":"crossref","unstructured":"Amerinia, I., Uricchioa, T., Ballana, L. et al.: Localization of jpeg double compression through multi-domain convolutional neural networks. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Los Alamitos: IEEE Computer Society Press, pp. 53\u201359 (2017)","DOI":"10.1109\/CVPRW.2017.233"},{"issue":"4","key":"892_CR17","doi-asserted-by":"publisher","first-page":"90","DOI":"10.4018\/IJDCF.2018100107","volume":"10","author":"R Wang","year":"2018","unstructured":"Wang, R., Wei, L., Li, J.: Digital image splicing detection based on Markov features in QDCT and QWT domain. J. Digital Crime Forensics 10(4), 90\u2013107 (2018)","journal-title":"J. Digital Crime Forensics"},{"key":"892_CR18","unstructured":"Yang, J., Zhang, K., Cui, Z., et al.: Inscon: Instance consistency feature representation via self-supervised learning. arXiv:2203.07688, pp. 1\u201317 (2022)"},{"issue":"3","key":"892_CR19","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1109\/LSP.2016.2641006","volume":"24","author":"L Bondi","year":"2017","unstructured":"Bondi, L., Baroffio, L., G\u00fcera, D.: First steps toward camera model identification with convolutional neural networks. IEEE Signal Process. Lett. 24(3), 259\u2013263 (2017)","journal-title":"IEEE Signal Process. Lett."},{"key":"892_CR20","doi-asserted-by":"crossref","unstructured":"Cozzolino, D., Gragnaniello, D., Verdoliva, L.: Image forgery localization through the fusion of camera-based, feature-based and pixel-based techniques. In: Proceedings of the 2014 IEEE International Conference on Image Processing. Los Alamitos: IEEE Computer Society Press, pp. 5302\u20135306 (2014)","DOI":"10.1109\/ICIP.2014.7026073"},{"key":"892_CR21","doi-asserted-by":"crossref","unstructured":"Liu, B., Pun, C.M.: Exposing splicing forgery in digital image by detecting noise discrepancies. In: Proceedings of the International Symposium on Communications and Information Technologies. Los Alamitos: IEEE Computer Society Press, pp. 1\u20134 (2013)","DOI":"10.7763\/IJCCE.2015.V4.378"},{"key":"892_CR22","first-page":"111","volume":"40","author":"WH Wang","year":"2019","unstructured":"Wang, W.H., Yan, Y.Y., Jiang, M.X.: Image denoising algorithm based on noise detection and dynamic window. J. Graph. 40, 111 (2019)","journal-title":"J. Graph."},{"key":"892_CR23","doi-asserted-by":"crossref","unstructured":"Ahmed, C.M., Ochoa, M., Zhou, J., et al.: Noiseprint: attack detection using sensor and process noise fingerprint in cyber physical systems. In: Proceedings of the 2018 on Asia Conference on Computer and Communications Security. Association for Computing Machinery, pp. 483\u2013497 (2018)","DOI":"10.1145\/3196494.3196532"},{"key":"892_CR24","doi-asserted-by":"crossref","unstructured":"Bappy, J. H., Roy-Chowdhury, A. K., Bunk, J., et al.: Exploiting spatial structure for localizing manipulated image regions. In: Proceedings of the 2017 IEEE International Conference on Computer Vision. Los Alamitos: IEEE Computer Society Press, pp. 4970\u20134979 (2017)","DOI":"10.1109\/ICCV.2017.532"},{"key":"892_CR25","doi-asserted-by":"crossref","unstructured":"Bappy, J. H., Simons, C., Nataraj, L.: Hybrid LSTM and encoder-decoder architecture for detection of image forgeries. arXiv:1903.02495, pp. 1\u201314 (2019)","DOI":"10.1109\/TIP.2019.2895466"},{"key":"892_CR26","doi-asserted-by":"crossref","unstructured":"Zhou, P., Han, X., Morariu, V.I.: Learning rich features for image manipulation detection. In: Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, pp. 1503\u20131601 (2018)","DOI":"10.1109\/CVPR.2018.00116"},{"issue":"5","key":"892_CR27","first-page":"1","volume":"7","author":"P Wang","year":"2015","unstructured":"Wang, P., Wei, Z., Xiao, L.: Pure spatial rich model features for digital image steganalysis. Multimed. Tools Appl. 7(5), 1\u201315 (2015)","journal-title":"Multimed. Tools Appl."},{"key":"892_CR28","doi-asserted-by":"crossref","unstructured":"Wu, Y., AbdAlmageed, W., Natarajan, P.: Mantra-net: Manipulation tracing network for detection and localization of image forgeries with anomalous features. In: Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, pp. 9543\u20139552 (2019)","DOI":"10.1109\/CVPR.2019.00977"},{"key":"892_CR29","doi-asserted-by":"crossref","unstructured":"Kwon, M.-J., Yu, I.-J., Nam, S.-H., et al.: Cat-net: compression artifact tracing network for detection and localization of image splicing. In: Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision. Los Alamitos: IEEE Computer Society Press, pp. 375\u2013384 (2021)","DOI":"10.1109\/WACV48630.2021.00042"},{"key":"892_CR30","doi-asserted-by":"crossref","unstructured":"Hu, X., Zhang, Z., Jiang, Z., et al.: Span: spatial pyramid attention network for image manipulation localization. In: Proceedings of the Computer Vision-ECCV 2020 the 16th European Conference. Los Alamitos: IEEE Computer Society Press, pp. 312\u2013328 (2020)","DOI":"10.1007\/978-3-030-58589-1_19"},{"key":"892_CR31","doi-asserted-by":"crossref","unstructured":"Yin, W., Lu, P., Zhao, Z., et al.: Yes, \"Attention Is All You Need\", for Exemplar based Colorization. In: Proceedings of the 29th ACM International Conference on Multimedia. ACM: Association for Computing Machinery, pp. 2243\u20132251 (2021)","DOI":"10.1145\/3474085.3475385"},{"key":"892_CR32","doi-asserted-by":"crossref","unstructured":"Wu, Z., Xiong, Y., Yu, S.X., et al.: Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, pp. 3733\u20133742 (2018)","DOI":"10.1109\/CVPR.2018.00393"},{"key":"892_CR33","unstructured":"Chen, T., Kornblith, S., Norouzi, M.: A simple framework for contrastive learning of visual representations. In: Proceedings of the 37th International Conference on Machine Learning. ACM: Association for Computing Machinery, pp. 1597\u20131607 (2020)"},{"key":"892_CR34","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., W, Y.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"892_CR35","unstructured":"Chen, X., Fan, H., Girshick, R., et al.: Improved baselines with momentum contrastive learning. arXiv:2003.04297, pp. 1\u20133 (2020)"},{"key":"892_CR36","doi-asserted-by":"crossref","unstructured":"Wang, T., Lu, J., Lai, Z. et al.: Uncertainty-guided voxel-level supervised contrastive learning for semi-supervised medical image segmentation. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. Los Alamitos: IEEE Computer Society Press, pp. 1444\u20131450 (2022)","DOI":"10.24963\/ijcai.2022\/201"},{"key":"892_CR37","doi-asserted-by":"crossref","unstructured":"Wang, X., Zhang, R., Shen, C., et al.: Dense contrastive learning for self-supervised visual pretraining. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, pp. 3024\u20133033 (2021)","DOI":"10.1109\/CVPR46437.2021.00304"},{"key":"892_CR38","doi-asserted-by":"crossref","unstructured":"Galdran, A., Chakor, H., Alrushood, A. A., et al.: Automatic classification and triage of diabetic retinopathy from retinal images based on a convolutional neural networks (CNN) method. In: Proceedings of the 2019 European Association for Vision and Eye Research Conference. ACM: Association for Computing Machinery, pp. 343\u2013344 (2019)","DOI":"10.1111\/j.1755-3768.2019.5391"},{"key":"892_CR39","doi-asserted-by":"crossref","unstructured":"Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the 2017 IEEE International Conference on Computer Vision. Los Alamitos: IEEE Computer Society Press, pp. 1501\u20131510 (2017)","DOI":"10.1109\/ICCV.2017.167"},{"key":"892_CR40","unstructured":"Touvron, H., Cord, M., Douze, M., et al.: Training data-efficient image transformers & distillation through attention. In: International Conference on Machine Learning. Los Alamitos: IEEE Computer Society Press, pp. 10347\u201310357 (2021)"},{"key":"892_CR41","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., et al. Rethinking atrous convolution for semantic image segmentation. arXiv:1706.05587, pp. 1\u201314 (2017)"},{"key":"892_CR42","doi-asserted-by":"crossref","unstructured":"Ma, J., Zhao, Z., Yi, X., et al.: Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Los Alamitos: IEEE Computer Society Press, pp. 1930\u20131939 (2018)","DOI":"10.1145\/3219819.3220007"},{"key":"892_CR43","unstructured":"Lepikhin, D., Lee, H., Xu, Y., et al.: GShard: scaling giant models with conditional computation and automatic sharding. In: Proceedings of the Ninth International Conference on Learning Representations, pp. 1\u201323 (2021)"},{"key":"892_CR44","unstructured":"Fedus, W., Zoph, B., Shazeer, N.: Switch transformers: scaling to trillion parameter models with simple and efficient sparsity. arXiv:2101.03961, pp. 1\u201340 (2022)"},{"key":"892_CR45","unstructured":"Du, N., Huang, Y., Dai, A.M., et al.: GLaM: efficient scaling of language models with mixture-of-experts. arXiv:2112.06905, pp. 1\u201323 (2022)"},{"key":"892_CR46","unstructured":"Zoph, B., Bello, I., Kumar, S., et al.: ST-MoE: Designing stable and transferable sparse expert models. arXiv preprint arXiv:2202.08906, pp. 1\u201338 (2022)"},{"key":"892_CR47","doi-asserted-by":"crossref","unstructured":"Dong, J., Wang, W., Tan, T.: CASIA image tampering detection evaluation database. In: Proceedings of the 2013 IEEE China Summit and International Conference on Signal and Information Processing. Los Alamitos: IEEE Computer Society Press, pp. 1\u20136 (2013)","DOI":"10.1109\/ChinaSIP.2013.6625374"},{"key":"892_CR48","unstructured":"Shon, S., Mun, S., Hansen, J.H.L., et al.: KU-ISPL language recognition system for NIST 2015 i-Vector machine learning challenge[J]. arXiv:1609.06404, pp. 1\u20135 (2016)"},{"key":"892_CR49","doi-asserted-by":"crossref","unstructured":"Wen, B., Zhu, Y., Subramanian, R., et al.: COVERAGE - A novel database for copy-move forgery detection. In: Proceedings of the 2016 IEEE International Conference on Image Processing. Los Alamitos: IEEE Computer Society Press, pp. 1\u20135 (2016)","DOI":"10.1109\/ICIP.2016.7532339"},{"key":"892_CR50","doi-asserted-by":"crossref","unstructured":"Novoz\u00e1msk\u00fd, A., Mahdian, B., Saic, S.: Extended IMD2020: a large-scale annotated dataset tailored for detecting manipulated images. In: Proceedings of the IEEE Winter Applications of Computer Vision Workshops 2020, pp. 71\u201380 (2020)","DOI":"10.1109\/WACVW50321.2020.9096940"},{"key":"892_CR51","doi-asserted-by":"crossref","unstructured":"Hao, J., Zhang, Z., Yang, S., et al.: TransForensics: image forgery localization with dense self-attention. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15055\u201315064 (2021)","DOI":"10.1109\/ICCV48922.2021.01478"},{"key":"892_CR52","unstructured":"Krawetz, N.. A picture\u2019s worth...Digital Image Analysis and Forensics Version 2. Hacker Factor Solutions, pp. 1\u201331 (2007)"},{"issue":"10","key":"892_CR53","doi-asserted-by":"publisher","first-page":"1497","DOI":"10.1016\/j.imavis.2009.02.001","volume":"27","author":"B Mahdian","year":"2009","unstructured":"Mahdian, B., Saic, S.: Using noise inconsistencies for blind image forensics. Image Vis. Comput. 27(10), 1497\u20131503 (2009)","journal-title":"Image Vis. Comput."},{"issue":"5","key":"892_CR54","doi-asserted-by":"publisher","first-page":"1566","DOI":"10.1109\/TIFS.2012.2202227","volume":"7","author":"P Ferrara","year":"2012","unstructured":"Ferrara, P., Bianchi, T., De Rosa, A., et al.: Image forgery localization via fine-grained analysis of cfa artifacts. IEEE Trans. Inf. Forensics Secur. 7(5), 1566\u20131577 (2012)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"892_CR55","doi-asserted-by":"crossref","unstructured":"Bappy, J. H., Roy-Chowdhury, A.K., Bunk, J., et al.: Exploiting spatial structure for localizing manipulated image regions. In: Proceedings of the IEEE International Conference on Computer Vision. Los Alamitos: IEEE Computer Society Press, pp. 4970\u20134979 (2017)","DOI":"10.1109\/ICCV.2017.532"},{"key":"892_CR56","doi-asserted-by":"crossref","unstructured":"Zhou, P., Han, X., Morariu, V. I., et al.: Learning rich features for image manipulation detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, pp. 1053\u20131061 (2018)","DOI":"10.1109\/CVPR.2018.00116"},{"key":"892_CR57","doi-asserted-by":"crossref","unstructured":"Niloy, F. F., Bhaumik, K. K., Woo, S.S.: CFL-Net: image forgery localization using contrastive learning. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. Los Alamitos: IEEE Computer Society Press, pp. 4642\u20134651 (2023)","DOI":"10.1109\/WACV56688.2023.00462"},{"issue":"1","key":"892_CR58","first-page":"26166","volume":"14","author":"M Zhu","year":"2024","unstructured":"Zhu, M., Li, M., Wang, Z.: Image tampering detection based on RDS-YOLOv5 feature enhancement transformation. Sci. Reports 14(1), 26166 (2024)","journal-title":"Sci. Reports"},{"key":"892_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.113033","volume":"310","author":"R Bai","year":"2025","unstructured":"Bai, R.: Weakly-supervised cross-contrastive learning network for image manipulation detection and localization. Knowl.-Based Syst. 310, 113033 (2025)","journal-title":"Knowl.-Based Syst."},{"key":"892_CR60","first-page":"260","volume-title":"Image Processing, Electronics and Computers","author":"Y Wang","year":"2024","unstructured":"Wang, Y., Liu, L., Huang, T.: Detection of image tampering using multiscale fusion and anomalousness assessment. In: Image Processing, Electronics and Computers, pp. 260\u2013270. IOS Press, Amsterdam (2024)"},{"key":"892_CR61","doi-asserted-by":"crossref","unstructured":"Yang, B.: A comprehensive research of image tampering detection techniques based on deep learning. In: 2024 International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2024). Paris: Atlantis Press, pp. 66\u201374 (2024)","DOI":"10.2991\/978-94-6463-518-8_8"},{"issue":"2","key":"892_CR62","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1007\/s11063-024-11448-9","volume":"56","author":"S Chakraborty","year":"2024","unstructured":"Chakraborty, S., Chatterjee, K., Dey, P.: Detection of image tampering using deep learning, error levels and noise residuals. Neural Process. Lett. 56(2), 112 (2024)","journal-title":"Neural Process. Lett."}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-00892-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-025-00892-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-00892-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T01:22:15Z","timestamp":1757208135000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-025-00892-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,8]]},"references-count":62,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["892"],"URL":"https:\/\/doi.org\/10.1007\/s44196-025-00892-7","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,8]]},"assertion":[{"value":"2 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 June 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 July 2025","order":5,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":6,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"In the Acknowledgements section of this article, the \u2018National Natural Science Foundation of China\u2019 was missing and has been added.","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"173"}}