Art-cam

The proliferation of generative artificial intelligence (AI) in visual arts has created a crisis of provenance, authorship attribution, and curatorial reproducibility. Traditional digital provenance models (e.g., CAI, blockchain-based registries) fail to capture the non-deterministic, latent-space-driven nature of AI-generated works. This paper introduces , a conceptual framework and software architecture designed as a "camera for artificial intelligence"—a continuous, auditable recording mechanism that captures the latent, parametric, and interactive states leading to a generative artwork. Unlike post-hoc watermarking or metadata tagging, Art-Cam functions as a native observer within the generative process, serializing prompt chains, seed values, model checkpoints, hyperparameters, and user interactions into a verifiable "generative trace." We argue that Art-Cam not only establishes a new standard for AI art provenance but also enables novel curatorial practices, including parametric curation, interactive replay, and forensic art criticism. Finally, we discuss implementation challenges, including computational overhead, model heterogeneity, and privacy concerns.

Art-Cam is not a single model but a middleware protocol. Figure 1 (described textually) illustrates four layers: art-cam

A software layer that intercepts, serializes, and cryptographically seals all state-modifying operations between a human user and a generative AI model, producing a verifiable Generative Trace File (GTF) that can be rendered independently. Figure 1 (described textually) illustrates four layers: A

, which was founded by former ArtCAM developers and offers a similar workflow with modern stability. Sarva Sudarsanaa Academy 2. Art Cam (Mobile Photo Editor Apps) Art Cam (Mobile Photo Editor Apps)