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Although recent advances have explored multimodal learning for fake news detection, existing models often fail to effectively integrate heterogeneous data sources and remain vulnerable to adversarial manipulations. To address these challenges, we propose  (Multimodal Adversarial Deep Semantic Learning), a robust multimodal fake news detection framework that unifies generative adversarial networks (GANs) with supervised contrastive learning. Specifically,  employs a multi-layer joint attention mechanism to align and fuse textual and visual features, while adversarial training encourages the extraction of event-invariant representations, enhancing generalizability across unseen news events. Additionally, contrastive learning with adversarial perturbations further strengthens feature discrimination and robustness against attacks. Extensive experiments on benchmark Twitter and Weibo datasets demonstrate that  achieves state-of-the-art accuracy (85.3%) and maintains stable performance with only a 1.1% drop under adversarial conditions, outperforming existing methods in both detection accuracy and resilience. These results underscore  \u2019s effectiveness in advancing robust multimodal fake news detection and promoting digital information integrity.<\/jats:p>","DOI":"10.1186\/s42400-026-00550-1","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T07:15:39Z","timestamp":1776064539000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fake news detection with GAN-augmented contrastive learning and multimodal attention"],"prefix":"10.1186","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7120-9003","authenticated-orcid":false,"given":"Cong","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yebo","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ju","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingting","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zijian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiahua","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Teng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,13]]},"reference":[{"key":"550_CR1","doi-asserted-by":"crossref","unstructured":"Ali H, Khan MS, AlGhadhban A, Alazmi M, Alzamil A, Al-Utaibi K, Qadir J (2021) All your fake detector are belong to us: evaluating adversarial robustness of fake-news detectors under black-box settings. 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