mlsanityData to 7 Oct 2026

AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Images

arXiv 2404.14244 ↗First posted 22 Apr 2024cs.CR cs.AI cs.CYcited by 10

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Abstract (arXiv)

Recent advances in the field of generative artificial intelligence (AI) have blurred the lines between authentic and machine-generated content, making it almost impossible for humans to distinguish between such media. One notable consequence is the use of AI-generated images for fake profiles on social media. While several types of disinformation campaigns and similar incidents have been reported in the past, a systematic analysis has been lacking. In this work, we conduct the first large-scale investigation of the prevalence of AI-generated profile pictures on Twitter. We tackle the challenges of a real-world measurement study by carefully integrating various data sources and designing a multi-stage detection pipeline. Our analysis of nearly 15 million Twitter profile pictures shows that 0.052% were artificially generated, confirming their notable presence on the platform. We comprehensively examine the characteristics of these accounts and their tweet content, and uncover patterns of coordinated inauthentic behavior. The results also reveal several motives, including spamming and political amplification campaigns. Our research reaffirms the need for effective detection and mitigation strategies to cope with the potential negative effects of generative AI in the future.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 60 of its 123 references that are in the map. First posted on arXiv 22 Apr 2024.

Builds on

60 of 123 references.

References · 60

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50 most cited of 60 All 60 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 4.

10 papers.

  1. 2024Examining the Prevalence and Dynamics of AI-Generated Media in Art Subredditscited by 8
  2. 2025TrueFake: A Real World Case Dataset of Last Generation Fake Images also Shared on Social Networkscited by 8
  3. 2024Adversarial Robustness of AI-Generated Image Detectors in the Real Worldcited by 6
  4. 2025So-Fake: Benchmarking and Explaining Social Media Image Forgery Detectioncited by 4
  5. 2025RAID: A Dataset for Testing the Adversarial Robustness of AI-Generated Image Detectorscited by 4
  6. 2025When Algorithms Play Favorites: Lookism in the Generation and Perception of Facescited by 2
  7. 2026Aesthetics as Structural Harm: Algorithmic Lookism Across Text-to-Image Generation and Classificationcited by 2
  8. 2026Quality-Aware Calibration for AI-Generated Image Detection in the Wildcited by 2
  9. 2026BIAS-ID: A Framework for Analyzing Transformation Biases in AI-Generated Image Detectorscited by 1
  10. 2026Training-Free Reconstruction-Based AI-Generated Image Detectors Are Inherently Vulnerable to Adversarial Examplescited by 0