{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T12:51:29Z","timestamp":1774702289152,"version":"3.50.1"},"reference-count":41,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFC3010302"],"award-info":[{"award-number":["2023YFC3010302"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013058","name":"Jiangsu Provincial Key Research and Development Program","doi-asserted-by":"publisher","award":["BE2023836"],"award-info":[{"award-number":["BE2023836"]}],"id":[{"id":"10.13039\/501100013058","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008081","name":"Southeast University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008081","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82571165"],"award-info":[{"award-number":["82571165"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.knosys.2026.115655","type":"journal-article","created":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T22:50:22Z","timestamp":1772405422000},"page":"115655","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["BM-DDFN: Bilinear cross-domain modeling for AIGC image source attribution"],"prefix":"10.1016","volume":"340","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6823-0306","authenticated-orcid":false,"given":"Zihan","family":"Qin","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3491-5640","authenticated-orcid":false,"given":"Siqi","family":"Gu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8116-6296","authenticated-orcid":false,"given":"Yuan","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7763-9492","authenticated-orcid":false,"given":"Lizhe","family":"Xie","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6191-3413","authenticated-orcid":false,"given":"Mang","family":"Jing","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2858-0104","authenticated-orcid":false,"given":"Qianyu","family":"Xiang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9077-3495","authenticated-orcid":false,"given":"Yunfeng","family":"Guo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7381-1211","authenticated-orcid":false,"given":"Zheng","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7431-7957","authenticated-orcid":false,"given":"Yining","family":"Hu","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.115655_bib0001","first-page":"2672","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.115655_bib0002","series-title":"International Conference on Machine Learning","first-page":"2256","article-title":"Deep unsupervised learning using nonequilibrium thermodynamics","author":"Sohl-Dickstein","year":"2015"},{"issue":"8","key":"10.1016\/j.knosys.2026.115655_bib0003","first-page":"1","article-title":"Video generation models as world simulators","volume":"1","author":"Brooks","year":"2024","journal-title":"OpenAI Blog"},{"key":"10.1016\/j.knosys.2026.115655_bib0004","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"4401","article-title":"A style-based generator architecture for generative adversarial networks","author":"Karras","year":"2019"},{"key":"10.1016\/j.knosys.2026.115655_bib0005","unstructured":"Y. Lu, B. Kakillioglu, S. Velipasalar, Autonomously and simultaneously refining deep neural network parameters by a bi-generative adversarial network aided genetic algorithm, (2018). arXiv preprint arXiv: 1809.10244."},{"key":"10.1016\/j.knosys.2026.115655_bib0006","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.115655_bib0007","unstructured":"A. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. McGrew, I. Sutskever, M. Chen, Glide: towards photorealistic image generation and editing with text-guided diffusion models, (2021). arXiv preprint arXiv: 2112.10741."},{"key":"10.1016\/j.knosys.2026.115655_bib0008","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"10684","article-title":"High-resolution image synthesis with latent diffusion models","author":"Rombach","year":"2022"},{"issue":"10","key":"10.1016\/j.knosys.2026.115655_bib0009","doi-asserted-by":"crossref","DOI":"10.23915\/distill.00003","article-title":"Deconvolution and checkerboard artifacts","volume":"1","author":"Odena","year":"2016","journal-title":"Distill"},{"issue":"5","key":"10.1016\/j.knosys.2026.115655_bib0010","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1109\/JSTSP.2020.3002101","article-title":"Media forensics and deepfakes: an overview","volume":"14","author":"Verdoliva","year":"2020","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"10.1016\/j.knosys.2026.115655_bib0011","series-title":"ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"1","article-title":"On the detection of synthetic images generated by diffusion models","author":"Corvi","year":"2023"},{"key":"10.1016\/j.knosys.2026.115655_bib0012","series-title":"International Conference on Machine Learning","first-page":"3247","article-title":"Leveraging frequency analysis for deep fake image recognition","author":"Frank","year":"2020"},{"key":"10.1016\/j.knosys.2026.115655_bib0013","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"7890","article-title":"Watch your up-convolution: CNN based generative deep neural networks are failing to reproduce spectral distributions","author":"Durall","year":"2020"},{"key":"10.1016\/j.knosys.2026.115655_bib0014","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"4195","article-title":"Scalable diffusion models with transformers","author":"Peebles","year":"2023"},{"key":"10.1016\/j.knosys.2026.115655_bib0015","unstructured":"S. McCloskey, M. Albright, Detecting GAN-generated imagery using color cues, (2018). arXiv preprint arXiv: 1812.08247."},{"key":"10.1016\/j.knosys.2026.115655_bib0016","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"5001","article-title":"Face x-ray for more general face forgery detection","author":"Li","year":"2020"},{"key":"10.1016\/j.knosys.2026.115655_bib0017","series-title":"European Conference on Computer Vision","first-page":"86","article-title":"Thinking in frequency: face forgery detection by mining frequency-aware clues","author":"Qian","year":"2020"},{"key":"10.1016\/j.knosys.2026.115655_bib0018","unstructured":"J. Chen, J. Yao, L. Niu, A single simple patch is all you need for AI-generated image detection, (2024). arXiv preprint arXiv: 2402.01123."},{"key":"10.1016\/j.knosys.2026.115655_bib0019","series-title":"Proceedings of the Computer Vision and Pattern Recognition Conference","first-page":"12830","article-title":"Fire: robust detection of diffusion-generated images via frequency-guided reconstruction error","author":"Chu","year":"2025"},{"issue":"14","key":"10.1016\/j.knosys.2026.115655_bib0020","doi-asserted-by":"crossref","first-page":"3134","DOI":"10.3390\/math11143134","article-title":"Review of image forensic techniques based on deep learning","volume":"11","author":"Shi","year":"2023","journal-title":"Mathematics"},{"key":"10.1016\/j.knosys.2026.115655_bib0021","series-title":"Proceedings of the 32nd ACM International Conference on Multimedia","first-page":"10910","article-title":"Gg-editor: locally editing 3D avatars with multimodal large language model guidance","author":"Xu","year":"2024"},{"key":"10.1016\/j.knosys.2026.115655_bib0022","unstructured":"D. Zhou, M. Li, Z. Yang, Y. Lu, Y. Xu, Z. Wang, Z. Huang, Y. Yang, BideDPO: conditional image generation with simultaneous text and condition alignment, (2025). arXiv preprint arXiv: 2511.19268."},{"issue":"12","key":"10.1016\/j.knosys.2026.115655_bib0023","doi-asserted-by":"crossref","first-page":"2379","DOI":"10.1364\/JOSAA.4.002379","article-title":"Relations between the statistics of natural images and the response properties of cortical cells","volume":"4","author":"Field","year":"1987","journal-title":"J. Opt. Soc. Am. A"},{"issue":"1","key":"10.1016\/j.knosys.2026.115655_bib0024","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1146\/annurev.neuro.24.1.1193","article-title":"Natural image statistics and neural representation","volume":"24","author":"Simoncelli","year":"2001","journal-title":"Annu. Rev. Neurosci."},{"issue":"10","key":"10.1016\/j.knosys.2026.115655_bib0025","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1109\/TIP.2008.2001399","article-title":"Practical Poissonian-Gaussian noise modeling and fitting for single-image raw-data","volume":"17","author":"Foi","year":"2008","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.knosys.2026.115655_bib0026","series-title":"2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905)","first-page":"60","article-title":"A non-local algorithm for image denoising","volume":"2","author":"Buades","year":"2005"},{"key":"10.1016\/j.knosys.2026.115655_bib0027","unstructured":"Y. Song, J. Sohl-Dickstein, D.P. Kingma, A. Kumar, S. Ermon, B. Poole, Score-based generative modeling through stochastic differential equations, (2020). arXiv preprint arXiv: 2011.13456."},{"key":"10.1016\/j.knosys.2026.115655_bib0028","first-page":"8780","article-title":"Diffusion models beat gans on image synthesis","volume":"34","author":"Dhariwal","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.115655_bib0029","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"12873","article-title":"Taming transformers for high-resolution image synthesis","author":"Esser","year":"2021"},{"key":"10.1016\/j.knosys.2026.115655_bib0030","first-page":"5775","article-title":"DPM-solver: a fast ODE solver for diffusion probabilistic model sampling in around 10 steps","volume":"35","author":"Lu","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.115655_bib0031","first-page":"26565","article-title":"Elucidating the design space of diffusion-based generative models","volume":"35","author":"Karras","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.115655_bib0032","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"1449","article-title":"Bilinear CNN models for fine-grained visual recognition","author":"Lin","year":"2015"},{"key":"10.1016\/j.knosys.2026.115655_bib0033","unstructured":"A. Brock, J. Donahue, K. Simonyan, Large scale GAN training for high fidelity natural image synthesis, (2018). arXiv preprint arXiv: 1809.11096."},{"key":"10.1016\/j.knosys.2026.115655_bib0034","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"10696","article-title":"Vector quantized diffusion model for text-to-image synthesis","author":"Gu","year":"2022"},{"key":"10.1016\/j.knosys.2026.115655_bib0035","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"8695","article-title":"CNN-generated images are surprisingly easy to spot...for now","author":"Wang","year":"2020"},{"key":"10.1016\/j.knosys.2026.115655_bib0036","unstructured":"D. Podell, Z. English, K. Lacey, A. Blattmann, T. Dockhorn, J. M\u00fcller, J. Penna, R. Rombach, SDXL: improving latent diffusion models for high-resolution image synthesis, (2023). arXiv preprint arXiv: 2307.01952."},{"key":"10.1016\/j.knosys.2026.115655_bib0037","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"8110","article-title":"Analyzing and improving the image quality of stylegan","author":"Karras","year":"2020"},{"key":"10.1016\/j.knosys.2026.115655_bib0038","unstructured":"K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, (2014). arXiv preprint arXiv: 1409.1556."},{"key":"10.1016\/j.knosys.2026.115655_bib0039","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.knosys.2026.115655_bib0040","series-title":"International Conference on Machine Learning","first-page":"6105","article-title":"EfficientNet: rethinking model scaling for convolutional neural networks","author":"Tan","year":"2019"},{"key":"10.1016\/j.knosys.2026.115655_bib0041","series-title":"Proceedings of the Computer Vision and Pattern Recognition Conference","first-page":"8245","article-title":"Community forensics: using thousands of generators to train fake image detectors","author":"Park","year":"2025"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126003953?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126003953?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T12:11:32Z","timestamp":1774699892000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126003953"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":41,"alternative-id":["S0950705126003953"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.115655","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"BM-DDFN: Bilinear cross-domain modeling for AIGC image source attribution","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.115655","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"115655"}}