How Spammers and Scammers Leverage AI-Generated Images on Facebook for Audience Growth
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Abstract (arXiv)
Much of the research and discourse on risks from artificial intelligence (AI) image generators, such as DALL-E and Midjourney, has centered around whether they could be used to inject false information into political discourse. We show that spammers and scammers - seemingly motivated by profit or clout, not ideology - are already using AI-generated images to gain significant traction on Facebook. At times, the Facebook Feed is recommending unlabeled AI-generated images to users who neither follow the Pages posting the images nor realize that the images are AI-generated, highlighting the need for improved transparency and provenance standards as AI models proliferate.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Mar 2024.
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Citing papers per year: 2024: 4; 2025: 5; 2026: 2.
11 papers.
- 2024Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Datacited by 20
- 2024Adversarial Robustness of AI-Generated Image Detectors in the Real Worldcited by 6
- 2025Neglected Risks: The Disturbing Reality of Children's Images in Datasets and the Urgent Call for Accountabilitycited by 6
- 2024Is computational creativity flourishing on the dead internet?cited by 1
- 2024Application of AI-based Models for Online Fraud Detection and Analysiscited by 1
- 2025Designing Effective Digital Literacy Interventions for Boosting Deepfake Discernmentcited by 1
- 2025A parameter-efficient three-branch architecture for multimodal misinformation detection with limited annotationscited by 1
- 2026Toward Accountable AI-Generated Content on Social Platforms: Steganographic Attribution and Multimodal Harm Detectioncited by 1
- 2025Prefill-Guided Thinking for zero-shot detection of AI-generated imagescited by 0
- 2025LAID: Lightweight AI-Generated Image Detection in Spatial and Spectral Domainscited by 0
- 2026Synthetic Sociality: How Generative Models Privatize the Social Fabriccited by 0
- 2026Synthetic Sociality: How Generative Models Privatize the Social Fabric13 May 2026
- 2026Toward Accountable AI-Generated Content on Social Platforms: Steganographic Attribution and Multimodal Harm Detection12 Apr 2026
- 2025A parameter-efficient three-branch architecture for multimodal misinformation detection with limited annotations23 Dec 2025
- 2025Designing Effective Digital Literacy Interventions for Boosting Deepfake Discernment31 Jul 2025
- 2025LAID: Lightweight AI-Generated Image Detection in Spatial and Spectral Domains7 Jul 2025
- 2025Prefill-Guided Thinking for zero-shot detection of AI-generated images20 May 2025
- 2025Neglected Risks: The Disturbing Reality of Children's Images in Datasets and the Urgent Call for Accountability20 Apr 2025
- 2024Adversarial Robustness of AI-Generated Image Detectors in the Real World2 Oct 2024
- 2024Application of AI-based Models for Online Fraud Detection and Analysis25 Sep 2024
- 2024Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data19 Jun 2024
- 2024Is computational creativity flourishing on the dead internet?3 Jun 2024