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The rapid evolution of deep learning, particularly generative models for images and videos, is transforming how visual media is created, edited, and distributed. At the same time, it raises urgent concerns about authenticity, provenance, and accountability. Synthetic media can be produced at scale, editing histories may be obscured, and verification is increasingly difficult for both humans and automated systems. Preserving trust in visual media therefore requires technical infrastructures that record, verify, and communicate how content was created and transformed.
This special issue invites submissions that treat AI governance, authenticity, and provenance as visual computing problems, with technical contributions in computer graphics, visualization, and visual analytics. We also welcome work from HCI, AI/ML, multimedia systems, and security when the core contribution centers on visual media. We particularly encourage submissions from the visual analytics community on interactive systems for exploring and communicating provenance and authenticity in visual media workflows. Visual media includes photographs, videos, generative imagery, rendered content, AR/VR/XR assets, 3D models, and scientific visualizations produced through complex toolchains.
Submissions may address algorithmic approaches (e.g., watermarking, signatures, perceptual cues, and visual forensics), system pipelines for provenance tracking and verification, or visualization and interaction techniques supporting human inspection and sensemaking. Strong submissions should include a substantial contribution in visual media, visualization, interaction, or system contribution and demonstrate relevance to real-world workflows such as journalism, publishing, media distribution, scientific visualization, legal and archival contexts, GLAM institutions, and creative production pipelines.
In this context, AI governance refers to visual computing systems that support transparency and accountability in visual media production and distribution, including provenance-aware pipelines, audit trails, disclosure and labeling interfaces, and emerging standards (e.g., C2PA, CAWG, IPTC, JPEG Trust, TDMRep). Papers must make a clear contribution to graphics or visualization systems and not simply summarize standards or advocate positions without a technical contribution.
Topics of interest include, but not limited to:
In addition to research papers, IEEE CG&A invites Perspective and Overview Paper (POP) submissions related to this theme. POP papers may include surveys, tutorials, state-of-the-art reports, or forward-looking perspectives that synthesize knowledge or explore emerging aspects of the CFP theme. For details, visit CG&A Author Information Page, but please submit your paper to this special issue rather than the regular POP queue.
For author information and guidelines on submission criteria, visit the Author’s Information Page. Please submit papers through the IEEE Author Portal and be sure to select the special issue or special section name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts.
In addition to submitting your paper to IEEE CG&A you are also encouraged to upload the data related to your paper to IEEE DataPort. IEEE DataPort is IEEE's data platform that supports the storage and publishing of datasets while also providing access to thousands of research datasets. Uploading your dataset to IEEE DataPort will strengthen your paper and will support research reproducibility. Your paper and the dataset can be linked, providing a good opportunity for you to increase the number of citations you receive. Data can be uploaded to IEEE DataPort prior to submitting your paper or concurrent with the paper submission. Thank you!
