Fake content detection

Detection encompasses computational and forensic approaches to identify false, manipulated, or synthetically generated content across text, images, video, and audio. Detection methods vary widely depending on the content modality and the specific manipulation technique involved.

Foundational surveys and taxonomy

• The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans — Comprehensive survey and typology of false information ecosystem including detection approaches across machine learning, systems, and hybrid methods; organizes 200+ papers by information type (fabricated, propaganda, hoaxes, rumors, clickbait, conspiracy theories, satire)

Key papers

• Disinformation 2.0 in the Age of AI: A Cybersecurity Perspective — perspective on defense-in-depth detection strategies against AI-enabled disinformation; proposes layered approach with detection at multiple network, device, and platform levels
• DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection — comprehensive review of detection methods for facial manipulations across multiple techniques

Related topics

• Deepfakes (specific content type)
• Face manipulation (specific content type)
• Fact-checking and corrections (human-led verification)
• Media Forensics (technical forensics approaches)