Authenticity Mechanisms and the Outcome Chain of Image Archive Publishing in the AI Era

With generative AI rapidly entering image production and publishing workflows, the threshold for creating, repairing, enhancing, and recomposing images has been greatly lowered. This transformation challenges the default assumption of authenticity on which traditional image archive publishing has long relied. In conventional cultural heritage publishing, readers conventionally assume that photographs of artifacts, grotto details, buildings, or ornaments maintain a stable connection with real objects. In the AI era, however, images may be generated, seamlessly spliced, stylistically transferred, restored, or reconstructed, making it necessary to explain why an image is trustworthy, how it was produced, and what relationship it maintains with the original object. Based on the previous conceptual discussion of "image archive publishing, " this article focuses on the authenticity risks faced by image archive publishing in the AI era, including authenticity crises, broken source chains, confused versions, ambiguous rights boundaries, and limited modes of reuse. It argues that authenticity should shift from an implicit premise to an explicit, institutionalized, and continuously explainable mechanism. The paper proposes a six-part framework: traceable sources, comparable versions, scholarly annotation, manageable rights, graded use, and explainable communication. These mechanisms respond respectively to the problems of provenance loss, version disorder, insufficient knowledge organization, unclear authorization, limited utilization, and public misunderstanding. The article further emphasizes that the key role of publishing institutions is not merely to release images, but to organize, explain, authenticate, and transform image archives into trusted upstream resources that can be traced, accessed, cited, and reused. This requires publishers to intervene earlier in resource design, metadata organization, version description, rights governance, and digital transformation. The study also discusses the shift from a book-product logic to a resource-engineering logic. A book remains an important public interface, but it should no longer be regarded as the endpoint of image archive publishing. Instead, image archives should be organized as a layered resource system consisting of original images, knowledge annotations, research samples, and public transformation interfaces. Such a system makes the image archive usable not only for printed publication, but also for databases, exhibitions, digital thematic collections, educational platforms, interactive systems, knowledge graphs, and future AI-assisted applications. The publication and research chain around the Yungang Grottoes provides an illustrative case. The organization of images in Complete Works of the Yungang Grottoes, together with later research on Yungang ornamentation, folk meanings, Buddhist sinicization, digital reconstruction, interaction design, and aesthetic cognition, shows how trustworthy image resources can generate sustained scholarly, educational, digital, and interdisciplinary outcomes. The case also demonstrates that the value of image archive publishing lies not only in preserving true images, but in enabling images to be continuously trusted, interpreted, compared, and transformed. The paper concludes that the core value of image archive publishing in the AI era lies in rebuilding visual trust through institutionalized authenticity verification, thereby providing publishing infrastructure for the long-term development and reuse of cultural heritage resources.