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Ambiguity learning and data correlation in multi-cross modal cyber-physical systems for detecting fake information

Author

Listed:
  • Haewon Byeon
  • Ghayth AlMahadin
  • Qusay Bsoul
  • Aadam Quraishi
  • Mukesh Soni
  • Shahi Raza Khan
  • Mohammad Shabaz

Abstract

This paper proposes an improved cyber-physical false information detection model based on cross-modal ambiguous learning. This work introduced the IC2 LFD model with an emphasis on ‘text-image’ ambiguity, focusing on the main goal of intelligent multi-cross modal data fake information identification. The experiment revealed that the significance of text and images differs in intelligent multi-cross modal data fake information detection. The effectiveness of the model is verified on the twitter dataset, showing a 6% accuracy improvement compared to the baseline model and a 1.6% performance improvement over detection methods without dynamic weight allocation.

Suggested Citation

  • Haewon Byeon & Ghayth AlMahadin & Qusay Bsoul & Aadam Quraishi & Mukesh Soni & Shahi Raza Khan & Mohammad Shabaz, 2025. "Ambiguity learning and data correlation in multi-cross modal cyber-physical systems for detecting fake information," Cyber-Physical Systems, Taylor & Francis Journals, vol. 11(4), pages 488-507, October.
    • Handle: RePEc:taf:tcybxx:v:11:y:2025:i:4:p:488-507
    DOI: 10.1080/23335777.2025.2467638
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