{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:59:00Z","timestamp":1777705140767,"version":"3.51.4"},"reference-count":23,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,1,10]]},"abstract":"<jats:p>An increasing number of fake news combining text, images and other forms of multimedia are spreading rapidly across social platforms, leading to misinformation and negative impacts. Therefore, the automatic identification of multimodal fake news has become an important research hotspot in academia and industry. The key to multimedia fake news detection is to accurately extract features of both text and visual information, as well as to mine the correlation between them. However, most of the existing methods merely fuse the features of different modal information without fully extracting intra- and inter-modal connections and complementary information. In this work, we learn physical tampered cues for images in the frequency domain to supplement information in the image space domain, and propose a novel multimodal frequency-aware cross-attention network (MFCAN) that fuses the representations of text and image by jointly modelling intra- and inter-modal relationships between text and visual information whin a unified deep framework. In addition, we devise a new cross-modal fusion block based on the cross-attention mechanism that can leverage inter-modal relationships as well as intra-modal relationships to complement and enhance the features matching of text and image for fake news detection. We evaluated our approach on two publicly available datasets and the experimental results show that our proposed model outperforms existing baseline methods.<\/jats:p>","DOI":"10.3233\/jifs-233193","type":"journal-article","created":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T11:18:45Z","timestamp":1699615125000},"page":"433-455","source":"Crossref","is-referenced-by-count":4,"title":["Multi-modality frequency-aware cross attention network for fake news detection"],"prefix":"10.1177","volume":"46","author":[{"given":"Wei","family":"Cui","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China"},{"name":"Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuerui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingsheng","family":"Shang","sequence":"additional","affiliation":[{"name":"Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"2","key":"10.3233\/JIFS-233193_ref1","doi-asserted-by":"crossref","first-page":"102025","DOI":"10.1016\/j.ipm.2019.03.004","article-title":"An overview of online fake news: Characterization, detection, and discussion","volume":"57","author":"Zhang","year":"2020","journal-title":"Information Processing & Management"},{"issue":"1","key":"10.3233\/JIFS-233193_ref2","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1038\/s41467-018-07761-2","article-title":"Influence of fake news in Twitter during the US presidential election","volume":"10","author":"Bovet","year":"2019","journal-title":"Nature Communications"},{"issue":"6","key":"10.3233\/JIFS-233193_ref4","doi-asserted-by":"crossref","first-page":"e0252830","DOI":"10.1371\/journal.pone.0252830","article-title":"Mitigating infodemics: The relationship between news exposure and trust and belief in COVID-19 fake news and social media spreading","volume":"16","author":"Melki","year":"2021","journal-title":"PLoS One"},{"key":"10.3233\/JIFS-233193_ref5","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1145\/3123266.3123454","article-title":"Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs, In Association for Computing Machinery","author":"Jin","year":"2017","journal-title":"Proceedings of the 25th ACM international conference on Multimedia"},{"issue":"3","key":"10.3233\/JIFS-233193_ref6","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1109\/TMM.2016.2617078","article-title":"Novel Visual and Statistical Image Features for Microblogs News Verification","volume":"19","author":"Jin","year":"2017","journal-title":"IEEE Transactions on Multimedia"},{"issue":"2","key":"10.3233\/JIFS-233193_ref12","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1609\/aaai.v35i2.16193","article-title":"Local Relation Learning for Face Forgery Detection","volume":"35","author":"Chen","year":"2021","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"6380","key":"10.3233\/JIFS-233193_ref18","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.1126\/science.aao2998","article-title":"The science of fake news","volume":"359","author":"Lazer","year":"2018","journal-title":"Science"},{"issue":"1","key":"10.3233\/JIFS-233193_ref20","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1145\/3137597.3137600","article-title":"Fake News Detection on Social Media: A Data Mining Perspective","volume":"19","author":"Shu","year":"2017","journal-title":"ACM SIGKDD Explorations Newsletter"},{"issue":"2","key":"10.3233\/JIFS-233193_ref21","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1257\/jep.31.2.211","article-title":"Social Media and Fake News in the Election","volume":"31","author":"Allcott","year":"2017","journal-title":"Journal of Economic Perspectives"},{"issue":"6","key":"10.3233\/JIFS-233193_ref22","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1002\/acp.3376","article-title":"\u2018Lyin\u2019Ted\u2019,\u2018Crooked Hillary\u2019, and \u2018Deceptive Donald\u2019: Language of Lies in the US Presidential Debates","volume":"31","author":"Bond","year":"2017","journal-title":"Applied Cognitive Psychology"},{"issue":"1","key":"10.3233\/JIFS-233193_ref26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/pra2.2015.145052010082","article-title":"Automatic deception detection: Methods for finding fake news","volume":"52","author":"Conroy","year":"2015","journal-title":"Proceedings of the Association for Information Science and Technology"},{"issue":"3","key":"10.3233\/JIFS-233193_ref28","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1089\/big.2020.0062","article-title":"FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media","volume":"8","author":"Shu","year":"2020","journal-title":"Big Data"},{"key":"10.3233\/JIFS-233193_ref34","doi-asserted-by":"crossref","unstructured":"Liu Y. and Wu Y.-F. , Early Detection of Fake News on Social Media Through Propagation Path Classification with Recurrent and Convolutional Networks, Proceedings of the AAAI Conference on Artificial Intelligence 32(1) (2018).","DOI":"10.1609\/aaai.v32i1.11268"},{"issue":"8","key":"10.3233\/JIFS-233193_ref45","doi-asserted-by":"crossref","first-page":"3308","DOI":"10.1109\/TCSVT.2020.3037662","article-title":"Deep convolutional neural network for identifying seam-carving forgery","volume":"31","author":"Nam","year":"2020","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"issue":"1","key":"10.3233\/JIFS-233193_ref47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13635-016-0047-y","article-title":"Double JPEG compression forensics based on a convolutional neural network","volume":"2016","author":"Wang","year":"2016","journal-title":"EURASIP Journal on Information Security"},{"key":"10.3233\/JIFS-233193_ref48","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.jvcir.2017.09.003","article-title":"Aligned and non-aligned double JPEG detection using convolutional neural networks","volume":"49","author":"Barni","year":"2017","journal-title":"Journal of Visual Communication and Image Representation"},{"issue":"1","key":"10.3233\/JIFS-233193_ref56","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/T-C.1974.223784","article-title":"Discrete Cosine Transform","volume":"C-23","author":"Ahmed","year":"1974","journal-title":"IEEE Transactions on Computers"},{"issue":"6","key":"10.3233\/JIFS-233193_ref57","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Communications of the ACM"},{"issue":"12","key":"10.3233\/JIFS-233193_ref60","doi-asserted-by":"crossref","first-page":"15545","DOI":"10.1007\/s11042-017-5132-9","article-title":"Verifying information with multimedia content on twitter","volume":"77","author":"Boididou","year":"2018","journal-title":"Multimedia Tools and Applications"},{"key":"10.3233\/JIFS-233193_ref65","first-page":"2425","article-title":"Vqa: Visual question answering, In","author":"Antol","year":"2015","journal-title":"Proceedings of the IEEE international conference on computer vision"},{"issue":"86","key":"10.3233\/JIFS-233193_ref67","first-page":"2579","article-title":"Visualizing Data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"Journal of Machine Learning Research"},{"issue":"5","key":"10.3233\/JIFS-233193_ref69","doi-asserted-by":"crossref","first-page":"1747","DOI":"10.1109\/TNNLS.2019.2927224","article-title":"A semisupervised recurrent convolutional attention model for human activity recognition","volume":"31","author":"Chen","year":"2019","journal-title":"IEEE transactions on neural networks and learning systems"},{"issue":"2","key":"10.3233\/JIFS-233193_ref70","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1109\/TCYB.2017.2647904","article-title":"An adaptive semisupervised feature analysis for video semantic recognition","volume":"48","author":"Luo","year":"2017","journal-title":"IEEE transactions on cybernetics"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-233193","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:43:00Z","timestamp":1777455780000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-233193"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,10]]},"references-count":23,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3233\/jifs-233193","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,10]]}}}