mlsanityData to 7 Oct 2026

Exploring the Use of Abusive Generative AI Models on Civitai

arXiv 2407.12876 ↗First posted 16 Jul 2024cs.SI cs.AI cs.CLcited by 9

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Abstract (arXiv)

The rise of generative AI is transforming the landscape of digital imagery, and exerting a significant influence on online creative communities. This has led to the emergence of AI-Generated Content (AIGC) social platforms, such as Civitai. These distinctive social platforms allow users to build and share their own generative AI models, thereby enhancing the potential for more diverse artistic expression. Designed in the vein of social networks, they also provide artists with the means to showcase their creations (generated from the models), engage in discussions, and obtain feedback, thus nurturing a sense of community. Yet, this openness also raises concerns about the abuse of such platforms, e.g., using models to disseminate deceptive deepfakes or infringe upon copyrights. To explore this, we conduct the first comprehensive empirical study of an AIGC social platform, focusing on its use for generating abusive content. As an exemplar, we construct a comprehensive dataset covering Civitai, the largest available AIGC social platform. Based on this dataset of 87K models and 2M images, we explore the characteristics of content and discuss strategies for moderation to better govern these platforms.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 12 of its 15 references that are in the map. First posted on arXiv 16 Jul 2024.

Builds on

12 of 15 references.

References · 12

  1. 2022A Holistic Approach to Undesired Content Detection in the Real Worldcited by 337
  2. 2023Art and the science of generative AI: A deeper divecited by 93
  3. 2023Supporting Qualitative Analysis with Large Language Models: Combining Codebook with GPT-3 for Deductive Codingcited by 82
  4. 2021Understanding and Creating Art with AI: Review and Outlookcited by 60
  5. 2023Typology of Risks of Generative Text-to-Image Modelscited by 55
  6. 2021Copyright in Generative Deep Learningcited by 29
  7. 2021Deepfake Videos in the Wild: Analysis and Detectioncited by 23
  8. 2022Are Deepfakes Concerning? Analyzing Conversations of Deepfakes on Reddit and Exploring Societal Implicationscited by 14
  9. 2022Identifying and Characterizing Active Citizens who Refute Misinformation in Social Mediacited by 5
  10. 2016Interplay between Social Influence and Network Centrality: A Comparative Study on Shapley Centrality and Single-Node-Influence Centralitycited by 4
  11. 2023A Study of Partisan News Sharing in the Russian invasion of Ukrainecited by 3
  12. 2023torchdistill Meets Hugging Face Libraries for Reproducible, Coding-Free Deep Learning Studies: A Case Study on NLPcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 6; 2026: 3.

9 papers.