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Artificial Intelligence Generated Content (AIGC) assisting image production triggers academic controversy widely in journalism, while attracts increasingly consideration by media agencies. Key issues involve misinformation, authenticity, semantic fidelity and interpretability. Now, Most AIGC tools operate as opaque “black boxes,” which make them difficult to meet the dual demands of content accuracy and semantic alignment in journalistic contexts, even cause ethical, sociotechnical and trusting dilemma. In this paper, the authors explore feasible pathways for controllable image production in the typical scenario of journalism – special coverage, and conduct two experiments based on coverage projects of China’s top media agency: (1) Experiment 1 focuses on cross-platform adaptability by applying standardized prompts to three coverage scenes, showing the significant disparities in semantic alignment, cultural specificity, and visual realism, which are largely influenced by training corpus bias and platform-level filtering policies. (2) In experiment 2, a human-in-the-loop modular pipeline is developed that combines high-precision segmentation (SAM, GroundingDINO), semantic alignment (BrushNet) and style regulating (Style-LoRA, Prompt-to-Prompt). It ensures the editorial fidelity by CLIP-based semantic scoring, NSFW/OCR/YOLO-based content filtering, and the embedding of verifiable content credentials. The traceable deployment of modular pipeline keeps the consistency of semantic representation and interpretation when producing images. Consequently, the authors propose a human-AI collaboration mechanism of AIGC assisted image production for special coverage, and corresponding evaluation principles is suggested in three main criteria: Character Identity Stability (CIS), Cultural Expression Accuracy (CEA) and User-Public Appropriateness (U-PA). This paper calls for systemic upgrades involving human-in-the-loop modelling, stylistic co-creation, and trust labelling mechanisms, and provides a practical framework for controllable and reliable AIGC deployment in image production of journalism.
