mlsanityData to 7 Oct 2026

Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities

arXiv 2405.00711 ↗First posted 25 Apr 2024cs.CL cs.AI cs.CYcited by 7

On the map: Diffusion models → Image generation, text-to-image and editing

Abstract (arXiv)

In recent years, generative artificial intelligence models, represented by Large Language Models (LLMs) and Diffusion Models (DMs), have revolutionized content production methods. These artificial intelligence-generated content (AIGC) have become deeply embedded in various aspects of daily life and work. However, these technologies have also led to the emergence of Fake Artificial Intelligence Generated Content (FAIGC), posing new challenges in distinguishing genuine information. It is crucial to recognize that AIGC technology is akin to a double-edged sword; its potent generative capabilities, while beneficial, also pose risks for the creation and dissemination of FAIGC. In this survey, We propose a new taxonomy that provides a more comprehensive breakdown of the space of FAIGC methods today. Next, we explore the modalities and generative technologies of FAIGC. We introduce FAIGC detection methods and summarize the related benchmark from various perspectives. Finally, we discuss outstanding challenges and promising areas for future research.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 183 of its 271 references that are in the map. First posted on arXiv 25 Apr 2024.

Builds on

183 of 271 references.

Closest ancestors · 10

Same direction, newest first

  1. 2024Diffusion Model-Based Image Editing: A Survey27 Feb
  2. 2024Detecting Multimedia Generated by Large AI Models: A Survey22 Jan
  3. 2024Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs22 Jan
  4. 2023Towards Real-time Text-driven Image Manipulation with Unconditional Diffusion Models10 Apr
  5. 2023T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models16 Feb
  6. 2023Adding Conditional Control to Text-to-Image Diffusion Models10 Feb
  7. 2022SINE: SINgle Image Editing with Text-to-Image Diffusion Models8 Dec
  8. 2022Paint by Example: Exemplar-based Image Editing with Diffusion Models23 Nov
  9. 2022Hierarchical Text-Conditional Image Generation with CLIP Latents13 Apr
  10. 2021DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation6 Oct

Other references · 173

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  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  5. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
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50 most cited of 173 All 173 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 4.

7 papers.

  1. 2025Survey on AI-Generated Media Detection: From Non-MLLM to MLLMcited by 12
  2. 2024MM-CamObj: A Comprehensive Multimodal Dataset for Camouflaged Object Scenarioscited by 10
  3. 2024Trustworthy Text-to-Image Diffusion Models: A Timely and Focused Surveycited by 6
  4. 2026ImageAttributionBench: How Far Are We from Generalizable Attribution?cited by 1
  5. 2026Many Ways to Be Fake: Benchmarking Fake News Detection Under Strategy-Driven AI Generationcited by 0
  6. 2026MG-RWKV: Multi-Grained Context-Aware RWKV for Temporal Forgery Localizationcited by 0
  7. 2026A survey of AI-generated voices and their detectioncited by 0

Similar, not cited

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  1. 2024Exploring AI Text Generation, Retrieval-Augmented Generation, and Detection Technologies: a Comprehensive Overview
  2. 2025Could AI Trace and Explain the Origins of AI-Generated Images and Text?
  3. 2025Delving into: the quantification of Ai-generated content on the internet (synthetic data)
  4. 2023A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?
  5. 2026Detecting AI-Generated Content on Social Media with Multi-modal Language Models
  6. 2023Counter Turing Test CT^2: AI-Generated Text Detection is Not as Easy as You May Think -- Introducing AI Detectability Index
  7. 2026Reasoning-Aware AIGC Detection via Alignment and Reinforcement
  8. 2024Under the Surface: Tracking the Artifactuality of LLM-Generated Data