Trustworthy AI and Misinformation

Detecting manipulated content, labelling news early, and making predictions that report their own uncertainty.

Machine learning systems deployed on social platforms are adversarial by nature: the content they judge is produced by people who know they are being judged. We work on detection under that pressure — multi-modal fake news detection across text and images, audio-visual deepfake detection that generalises to unseen domains, and labelling news as early in its propagation as accuracy allows.

A second thread runs through all of it: a model should know what it does not know. We study epistemic uncertainty in prediction, so that a system can abstain instead of asserting.

Publications in this area

Teacher-Student Structure for Domain Adaptation in Ensemble Audio-Visual Video Deepfake Detection

Elham Abolhasani, Maryam Ramezani, Hamid R. Rabiee

arXiv preprint, 2026Preprint

arXiv

BibTeX

@misc{abolhasani2026teacher,
  title = {{Teacher-Student Structure for Domain Adaptation in Ensemble Audio-Visual Video Deepfake Detection}},
  author = {Elham Abolhasani and Maryam Ramezani and Hamid R. Rabiee},
  year = {2026},
  eprint = {2606.15117},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2606.15117}
}

Epistemic Uncertainty-aware Recommendation Systems via Bayesian Deep Ensemble Learning

Radin Cheraghi, Amir Mohammad Mahfoozi, Sepehr Zolfaghari, Mohammadshayan Shabani, Maryam Ramezani, Hamid R. Rabiee

arXiv preprint, 2025Preprint

arXiv

BibTeX

@misc{cheraghi2025epistemic,
  title = {{Epistemic Uncertainty-aware Recommendation Systems via Bayesian Deep Ensemble Learning}},
  author = {Radin Cheraghi and Amir Mohammad Mahfoozi and Sepehr Zolfaghari and Mohammadshayan Shabani and Maryam Ramezani and Hamid R. Rabiee},
  year = {2025},
  eprint = {2504.10753},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2504.10753}
}

FNR: A Similarity and Transformer-Based Approach to Detect Multi-Modal Fake News in Social Media

Faeze Ghorbanpour, Maryam Ramezani, Mohammad Amin Fazli, Hamid R. Rabiee

Social Network Analysis and Mining (SNAM), 2023Cited by 37

DOI arXiv

BibTeX

@article{ghorbanpour2023similarity,
  title = {{FNR: A Similarity and Transformer-Based Approach to Detect Multi-Modal Fake News in Social Media}},
  author = {Faeze Ghorbanpour and Maryam Ramezani and Mohammad Amin Fazli and Hamid R. Rabiee},
  journal = {Social Network Analysis and Mining},
  year = {2023},
  doi = {10.1007/s13278-023-01065-0},
  eprint = {2112.01131},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2112.01131}
}

News Labeling as Early as Possible: Real or Fake?

Maryam Ramezani, Mina Rafiei, Soroush Omranpour, Hamid R. Rabiee

IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2019Cited by 2

arXiv

BibTeX

@inproceedings{ramezani2019news,
  title = {{News Labeling as Early as Possible: Real or Fake?}},
  author = {Maryam Ramezani and Mina Rafiei and Soroush Omranpour and Hamid R. Rabiee},
  booktitle = {IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining},
  year = {2019},
  eprint = {1906.03423},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/1906.03423}
}