{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T02:56:36Z","timestamp":1764125796936,"version":"3.46.0"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-08043-7","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T02:51:45Z","timestamp":1764125505000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MIRC-FADNet: a mutual information-regularized constraints and frequency attention detection network for generative image forgery"],"prefix":"10.1007","volume":"81","author":[{"given":"Lulu","family":"Wen","sequence":"first","affiliation":[]},{"given":"Xing","family":"He","sequence":"additional","affiliation":[]},{"given":"Zikun","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Lin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Qian","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,26]]},"reference":[{"key":"8043_CR1","unstructured":"Tero K, Timo A, Samuli L, Jaakko L, et al. (2018) Progressive growing of GANs for improved quality, stability, and variation.[C], International Conference on Learning Representations"},{"key":"8043_CR2","doi-asserted-by":"crossref","unstructured":"Xu Zhang, Svebor Karaman, Shih-Fu Chang. (2019) Detecting and simulating artifacts in GAN fake images.[J], Computing Research Repository, 1\u20136.","DOI":"10.1109\/WIFS47025.2019.9035107"},{"key":"8043_CR3","unstructured":"Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky. (2018) Deep image prior[J], 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"8043_CR4","unstructured":"Tero K, Miika A, Timo A, Samuli L, et al. (2022) Elucidating the design space of diffusion-based generative models.[C], Conference on Neural Information Processing Systems"},{"key":"8043_CR5","unstructured":"Jonathan Ho, Ajay Jain, Pieter Abbeel. (2020) Denoising Diffusion Probabilistic Models[J], Computing Research Repository, 6840\u20136851."},{"key":"8043_CR6","unstructured":"Prafulla D, Heewoo J, Christine P, Jong W K, Alec R, Ilya S, et al. (2020) Jukebox: a generative model for music[J], Computing Research Repository, abs\/2005.00341"},{"key":"8043_CR7","unstructured":"Olaf Ronneberger, Philipp Fischer, Thomas Brox. (2017) U-Net: convolutional networks for biomedical image segmentation.[C], Medical Image Computing and Computer-Assisted Intervention: 3\u20133."},{"key":"8043_CR8","doi-asserted-by":"crossref","unstructured":"Sheng-Yu W, Oliver W, Richard Z, Andrew O, Alexei A E, et al. (2020) CNN-generated images are surprisingly easy to spot... For now.[J], Computing Research Repository, 8692\u20138701.","DOI":"10.1109\/CVPR42600.2020.00872"},{"issue":"12","key":"8043_CR9","doi-asserted-by":"publisher","first-page":"4217","DOI":"10.1109\/TPAMI.2020.2970919","volume":"43","author":"K Tero","year":"2021","unstructured":"Tero K, Samuli L, Timo A (2021) A style-based generator architecture for generative adversarial networks. IEEE Trans Pattern Anal Mach Intell 43(12):4217\u20134228","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8043_CR10","unstructured":"Sergey Sinitsa, Ohad Fried (2023). Deep image fingerprint: accurate and low budget synthetic image detector[J], Computing Research Repository, abs\/2303.10762"},{"key":"8043_CR11","doi-asserted-by":"crossref","unstructured":"Robin R, Andreas B, Dominik L, Bjoern O, et al. (2022) High-Resolution Image Synthesis with Latent Diffusion Models[C], Computer Vision and Pattern Recognition, 10674\u201310685","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"8043_CR12","doi-asserted-by":"crossref","unstructured":"Chengdong Dong, Ajay Kumar, Eryun Liu. (2022) Think twice before detecting GAN-generated fake images from their spectral domain imprints[J], Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops, 7855\u20137864.","DOI":"10.1109\/CVPR52688.2022.00771"},{"key":"8043_CR13","doi-asserted-by":"crossref","unstructured":"Seongmin L, Benjamin H, Hendrik S, Zijie J W, ShengYun P, Austin W, Kevin L, Haekyu P, Haoyang Y, Duen H ( C, et al. (2024) Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion[C], Visualization (IEEE 96\u2013100.","DOI":"10.1109\/VIS55277.2024.00027"},{"key":"8043_CR14","unstructured":"Jyoti A, Alex S, Jan K, Arash V, et al. (2021) A contrastive learning approach for training variational autoencoder priors[C], Conference on Neural Information Processing Systems"},{"key":"8043_CR15","doi-asserted-by":"crossref","unstructured":"Oran G, Adam P, Oron A, Shelly S, Devi P, Yaniv T, et al. (2022) Make-a-scene: scene-based text-to-image generation with human priors.[J], Computing Research Repository, 13675: 89\u2013106","DOI":"10.1007\/978-3-031-19784-0_6"},{"key":"8043_CR16","doi-asserted-by":"crossref","unstructured":"Lvmin Zhang, Anyi Rao, Maneesh Agrawala. (2023) Adding Conditional Control to Text-to-Image Diffusion Models[J], Computing Research Repository, 3836\u20133847","DOI":"10.1109\/ICCV51070.2023.00355"},{"key":"8043_CR17","doi-asserted-by":"crossref","unstructured":"Chaoyang X, Yuanfei D, Renjie L, Shiping W, et al. (2020) Deep Clustering by Maximizing Mutual Information in Variational Auto-Encoder. [J], Knowledge-based systems, 205: 106260\u2013106260.","DOI":"10.1016\/j.knosys.2020.106260"},{"key":"8043_CR18","doi-asserted-by":"crossref","unstructured":"Lakshmanan N, Tajuddin M M, Shivkumar C, Arjuna F, Jawadul H B, Amit K R, B. S M, et al. Detecting GAN Generated Fake Images Using Co-occurrence Matrices[J], Computing Research Repository, 2019, 31(5): 532\u20137.","DOI":"10.2352\/ISSN.2470-1173.2019.5.MWSF-532"},{"key":"8043_CR19","doi-asserted-by":"crossref","unstructured":"Ricard Durall, Margret Keuper, Janis Keuper. (2020) Watch Your Up-Convolution: CNN Based Generative Deep Neural Networks Are Failing to Reproduce Spectral Distributions.[C], Computer Vision and Pattern Recognition, 7887\u20137896","DOI":"10.1109\/CVPR42600.2020.00791"},{"key":"8043_CR20","doi-asserted-by":"crossref","unstructured":"Davide C, Giovanni P, Riccardo C, Matthias N, Luisa V, et al. (2024) Raising the bar of AI-generated image detection with CLIP[J], Computer Vision and Pattern Recognition, 4356\u20134366","DOI":"10.1109\/CVPRW63382.2024.00439"},{"key":"8043_CR21","doi-asserted-by":"crossref","unstructured":"Sara M, Nicol O B, Paolo B, Stefano T, et al. (2022) Detecting gan-generated images by orthogonal training of multiple CNNs[J], ICIP, 3091\u20133095","DOI":"10.1109\/ICIP46576.2022.9897310"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-08043-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-08043-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-08043-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T02:51:49Z","timestamp":1764125509000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-08043-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,26]]},"references-count":21,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["8043"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-08043-7","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,26]]},"assertion":[{"value":"15 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2025","order":3,"name":"first_online","label":"First Online","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":"Competing interest"}}],"article-number":"1596"}}