mlsanityData to 7 Oct 2026

Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News

arXiv 2009.07698 ↗First posted 16 Sep 2020cs.AI cs.CL cs.CVcited by 41

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Abstract (arXiv)

Large-scale dissemination of disinformation online intended to mislead or deceive the general population is a major societal problem. Rapid progression in image, video, and natural language generative models has only exacerbated this situation and intensified our need for an effective defense mechanism. While existing approaches have been proposed to defend against neural fake news, they are generally constrained to the very limited setting where articles only have text and metadata such as the title and authors. In this paper, we introduce the more realistic and challenging task of defending against machine-generated news that also includes images and captions. To identify the possible weaknesses that adversaries can exploit, we create a NeuralNews dataset composed of 4 different types of generated articles as well as conduct a series of human user study experiments based on this dataset. In addition to the valuable insights gleaned from our user study experiments, we provide a relatively effective approach based on detecting visual-semantic inconsistencies, which will serve as an effective first line of defense and a useful reference for future work in defending against machine-generated disinformation.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Sep 2020.

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Citing papers per year: 2021: 6; 2022: 9; 2023: 14; 2024: 7; 2025: 2; 2026: 3.

41 papers.

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  1. 2023A Watermark for Large Language Modelscited by 667
  2. 2023Can LLM-Generated Misinformation Be Detected?cited by 173
  3. 2023AI Transparency in the Age of LLMs: A Human-Centered Research Roadmapcited by 119
  4. 2021NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Mediacited by 106
  5. 2022End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Modelscited by 105
  6. 2024Detecting Multimedia Generated by Large AI Models: A Surveycited by 73
  7. 2023On the Learnability of Watermarks for Language Modelscited by 72
  8. 2021Open-Domain, Content-based, Multi-modal Fact-checking of Out-of-Context Images via Online Resourcescited by 71
  9. 2023Multimodal Automated Fact-Checking: A Surveycited by 51
  10. 2021A Survey on Multimodal Disinformation Detectioncited by 47
  11. 2022Attribution and Obfuscation of Neural Text Authorship: A Data Mining Perspectivecited by 40
  12. 2023Fighting Fire with Fire: Can ChatGPT Detect AI-generated Text?cited by 39
29 more
  1. 2023ConDA: Contrastive Domain Adaptation for AI-generated Text Detectioncited by 37
  2. 2021COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learningcited by 26
  3. 2023Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?cited by 21
  4. 2023Support or Refute: Analyzing the Stance of Evidence to Detect Out-of-Context Mis- and Disinformationcited by 21
  5. 2024Multimodal Misinformation Detection using Large Vision-Language Modelscited by 21
  6. 2023COVID-VTS: Fact Extraction and Verification on Short Video Platformscited by 15
  7. 2022DisinfoMeme: A Multimodal Dataset for Detecting Meme Intentionally Spreading Out Disinformationcited by 13
  8. 2024Detecting Misinformation in Multimedia Content through Cross-Modal Entity Consistency: A Dual Learning Approachcited by 12
  9. 2022Fact-Saboteurs: A Taxonomy of Evidence Manipulation Attacks against Fact-Verification Systemscited by 11
  10. 2024Machine-Generated Text Localizationcited by 11
  11. 2021Multi-Modal Semantic Inconsistency Detection in Social Media News Postscited by 10
  12. 2023Robust Domain Misinformation Detection via Multi-modal Feature Alignmentcited by 10
  13. 2025Increasing the Robustness of the Fine-tuned Multilingual Machine-Generated Text Detectorscited by 8
  14. 2022An Overview of Recent Work in Media Forensics: Methods and Threatscited by 7
  15. 2023Out-of-distribution Evidence-aware Fake News Detection via Dual Adversarial Debiasingcited by 7
  16. 2024Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunitiescited by 7
  17. 2022Collecting The Puzzle Pieces: Disentangled Self-Driven Human Pose Transfer by Permuting Texturescited by 6
  18. 2023GPT-4V(ision) as A Social Media Analysis Enginecited by 6
  19. 2026The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detectioncited by 6
  20. 2022NewsStories: Illustrating articles with visual summariescited by 5
  21. 2022Misinformation Detection in Social Media Video Postscited by 4
  22. 2024Exposing Text-Image Inconsistency Using Diffusion Modelscited by 4
  23. 2021Show, Write, and Retrieve: Entity-aware Article Generation and Retrievalcited by 3
  24. 2025CrossCheck-Bench: Diagnosing Compositional Failures in Multimodal Conflict Resolutioncited by 3
  25. 2022Synthetic Text Detection: Systemic Literature Reviewcited by 2
  26. 2024Image-Text Out-Of-Context Detection Using Synthetic Multimodal Misinformationcited by 2
  27. 2023Studying the impacts of pre-training using ChatGPT-generated text on downstream taskscited by 0
  28. 2026ForensicFormer: Hierarchical Multi-Scale Reasoning for Cross-Domain Image Forgery Detectioncited by 0
  29. 2026Is a Picture Worth a Thousand Words? Adaptive Multimodal Fact-Checking with Visual Evidence Necessitycited by 0