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  • Summary
  • Threat Model
  • Data
  • Qualitative + Quantitative Analysis + Case study
  1. Security
  2. IllicitPromotion

Seeing is Not Always Believing: An Empirical Analysis of Fake Evidence Generators

PreviousUnderstanding Cross-Platform Referral Traffic for Illicit Drug PromotionNextUnveiling the Risks of NFT Promotion Scams

Last updated 2 months ago

Link: https://ieeexplore.ieee.org/document/10628523

Conference: EuroS&P 2024

Keywords: Fake Evidence, Deepfake, Image Forensics

Summary

Build detector and conduct a empirical study on the ecosystem of fake evidence generators (FEGENs).

Threat Model

FEGEN

Data

  2. Search on google, facebook, and tiktok. Get 148 instances with 72 AAS websites, and 76 software tools, corresponding to the 125 potential FEGENs

Qualitative + Quantitative Analysis + Case study

(Note: Telegram search: SuperIndex News, Telegram Channels, TGStat)

Review public reports to get categories of potential FEGENs

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