LATIDIA · Ciberseguridad

Rethinking Visual Provenance: Detection and Watermarking Across Direct Visual Generation and LLM-Driven Code Rendering

arXiv:2610.08137v1 Tipo de anuncio: nuevo Resumen: los sistemas de IA crean imágenes y videos con modelos de generación de imágenes/videos o escribiendo códigos y descripciones gráficas que luego se renderizan. Estas rutas pueden producir

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arXiv:2610.08137v1 Announce Type: new Abstract: AI systems create images and videos with image/video generation models or by writing code and graphics descriptions that are then rendered. These routes can produce similar visible artifacts but expose different representations, intervention points, and provenance evidence. We develop a production-centered framework that compares detection and watermarking across both routes. An explicit verification specification distinguishes passive inference, message recovery, and authenticated provenance. We organize image, video, source-code, and rendering-aware watermarks by production stage. We examine the different requirements of generated images and video, plots and SVG, programmable video, and agent-composed workflows. Documented Claude, OpenAI, and rendering-tool interfaces connect the framework to concrete systems. We pose