mlsanityData to 7 Oct 2026

Towards a New Science of Disinformation

arXiv 2204.01489 ↗First posted 17 Mar 2022cs.CY cs.AI cs.SIcited by 0

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Abstract (arXiv)

How can we best address the dangerous impact that deep learning-generated fake audios, photographs, and videos (a.k.a. deepfakes) may have in personal and societal life? We foresee that the availability of cheap deepfake technology will create a second wave of disinformation where people will receive specific, personalized disinformation through different channels, making the current approaches to fight disinformation obsolete. We argue that fake media has to be seen as an upcoming cybersecurity problem, and we have to shift from combating its spread to a prevention and cure framework where users have available ways to verify, challenge, and argue against the veracity of each piece of media they are exposed to. To create the technologies behind this framework, we propose that a new Science of Disinformation is needed, one which creates a theoretical framework both for the processes of communication and consumption of false content. Key scientific and technological challenges facing this research agenda are listed and discussed in the light of state-of-art technologies for fake media generation and detection, argument finding and construction, and how to effectively engage users in the prevention and cure processes.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 98 references that are in the map. First posted on arXiv 17 Mar 2022.

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15 of 98 references.

References · 15

  1. 2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,036
  2. 2020Face2Face: Real-time Face Capture and Reenactment of RGB Videoscited by 631
  3. 2019Defending Against Neural Fake Newscited by 553
  4. 2019GLTR: Statistical Detection and Visualization of Generated Textcited by 490
  5. 2019The Deepfake Detection Challenge (DFDC) Preview Datasetcited by 208
  6. 2020Media Forensics and DeepFakes: an overviewcited by 208
  7. 2018Capsule-Forensics: Using Capsule Networks to Detect Forged Images and Videoscited by 180
  8. 2019FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signalscited by 143
  9. 2019Recurrent Convolutional Strategies for Face Manipulation Detection in Videoscited by 118
  10. 2019A Large-scale Dataset for Argument Quality Ranking: Construction and Analysiscited by 66
  11. 2019Exploring the Role of Prior Beliefs for Argument Persuasioncited by 30
  12. 2017Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Electioncited by 26
3 more
  1. 2015Humans Are Easily Fooled by Digital Imagescited by 8
  2. 2019Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cuescited by 8
  3. 2020FakeYou! -- A Gamified Approach for Building and Evaluating Resilience Against Fake Newscited by 1

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