近年來，人工智慧（AI）生成影像的快速發展已大幅改變視覺傳播的方式，並逐漸被應用於新聞報導之中。雖然傳統新聞媒體對於 AI 或經數位修改的影像多設有相關規範，但許多中小型媒體與社群平台已開始採用 AI 生成影像作為新聞配圖，進而引發關於新聞可信度與受眾感知的討論。儘管部分新聞類型過去便曾使用人工繪製插圖或電腦生成影像，然而 AI 生成影像往往與真實照片難以區分，因此帶來了新的倫理議題。
為了解受眾對新聞中 AI 生成影像的看法，本研究探討 64 位讀者如何評估五種新聞類型（政治、商業、娛樂、社會及科技）中的 AI 生成影像與真實影像。
研究結果顯示，真實影像在接受度方面顯著高於 AI 生成影像。此外，在所有新聞類型中，AI 生成的非人物影像之接受度皆高於 AI 生成人物影像。當影像被標示為 AI 生成時，其接受度會顯著下降。在各新聞類型中，政治新聞對 AI 生成影像的接受度最低，無論影像是否標示其 AI 來源皆是如此；相較之下，科技新聞則獲得最高的接受度評價。此外，當影像的 AI 來源未被揭露時，不同新聞類型之間的接受度差異更為明顯。
本研究結果可為考慮將 AI 應用於新聞製作的媒體從業人員提供實務參考，亦可作為政策制定者在研擬 AI 影像標示與資訊透明化相關規範時的重要依據。

Recent advances in AI-generated images have transformed visual communication, with increasing use in news reporting. While traditional news outlets maintain guidelines regarding AI or digitally altered images, many smaller media and social platforms have adopted AI-generated visuals, raising questions about credibility and audience perception. Although certain news genres have relied on human-made illustrations or computer-generated images, AI-generated images which are often indistinguishable from real photographs introduce new ethical concerns.
To understand audience perception of AI-generated images in news, this study examined how 64 readers evaluated AI-generated and real images across five news genres (politics, business, entertainment, society, and technology).
Results revealed that real images were rated significantly higher in terms of acceptance than AI-generated images, with AI non-human images rated higher than AI human images across all genres. AI labeling significantly reduced acceptance. Political news received the lowest levels of acceptance for AI-generated imagery, both when the images were unlabeled and when images were explicitly identified as AI-generated. In contrast, technology news received the highest acceptance ratings. Differences across news genres were more pronounced when the images' AI origin was not disclosed.
These findings offer practical implications for media professionals considering AI adoption in news production, and for policymakers developing guidelines around AI image labeling and transparency.

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