# Human-AI Collaboration Mechanism Study on AIGC Assisted Image Production for Special Coverage

arXiv: https://arxiv.org/abs/2512.13739 · HTML: https://mlsanity.com/paper/2512.13739/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 18 references that are in the map.
- First posted on arXiv 14 Dec 2025.

## Builds on (most cited first, 10 of 10)

- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT](https://mlsanity.com/paper/2303.04226/) (2023)
- [Enhancing Semantic Fidelity in Text-to-Image Synthesis: Attention Regulation in Diffusion Models](https://mlsanity.com/paper/2403.06381/) (2024)
- [Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?](https://mlsanity.com/paper/2402.15467/) (2024)
- [Interpretable and Reliable Detection of AI-Generated Images via Grounded Reasoning in MLLMs](https://mlsanity.com/paper/2506.07045/) (2025)
- [Guiding the Way: A Comprehensive Examination of AI Guidelines in Global Media](https://mlsanity.com/paper/2405.04706/) (2024)
- [ASAP: Interpretable Analysis and Summarization of AI-generated Image Patterns at Scale](https://mlsanity.com/paper/2404.02990/) (2024)
- [Characterizing AI-Generated Misinformation on Social Media](https://mlsanity.com/paper/2505.10266/) (2025)
- [Extended Creativity: A Conceptual Framework for Understanding Human-AI Creative Relations](https://mlsanity.com/paper/2506.10249/) (2025)

## Abstract (arXiv)

Artificial Intelligence Generated Content (AIGC) assisting image production triggers controversy in journalism while attracting attention from media agencies. Key issues involve misinformation, authenticity, semantic fidelity, and interpretability. Most AIGC tools are opaque "black boxes," hindering the dual demands of content accuracy and semantic alignment and creating ethical, sociotechnical, and trust dilemmas. This paper explores pathways for controllable image production in journalism's special coverage and conducts two experiments with projects from China's media agency: (1) Experiment 1 tests cross-platform adaptability via standardized prompts across three scenes, revealing disparities in semantic alignment, cultural specificity, and visual realism driven by training-corpus bias and platform-level filtering. (2) Experiment 2 builds a human-in-the-loop modular pipeline combining high-precision segmentation (SAM, GroundingDINO), semantic alignment (BrushNet), and style regulating (Style-LoRA, Prompt-to-Prompt), ensuring editorial fidelity through CLIP-based semantic scoring, NSFW/OCR/YOLO filtering, and verifiable content credentials. Traceable deployment preserves semantic representation. Consequently, we propose a human-AI collaboration mechanism for AIGC assisted image production in special coverage and recommend evaluating Character Identity Stability (CIS), Cultural Expression Accuracy (CEA), and User-Public Appropriateness (U-PA).