# Whose Image Is It?

**Track:** Imaging — beginner / high school — Digital & Generative Imaging — proposed
**Framework / surface:** ij8 Studio (literacy capstone)
**Level:** Beginner
**Prerequisites:** none

**In one line:** Provenance (C2PA/SynthID), bias audits, copyright, responsible disclosure.

## Theory, aesthetics & inspiration

Every generated image arrives with questions of origin and responsibility. Provenance standards like C2PA, from the Content Authenticity Initiative, attach a verifiable history to a file, while Google DeepMind's SynthID embeds an invisible watermark marking an image as machine-made. Models inherit the biases of their training data — Joy Buolamwini and Timnit Gebru's Gender Shades exposed how systems fail unevenly across skin tones, and Bender, Gebru and colleagues' "On the Dangers of Stochastic Parrots" warned of scale outrunning scrutiny. Copyright and authorship remain unsettled. Walter Benjamin foresaw the stakes in 1935, asking what becomes of a work's "aura" once it is endlessly reproduced — and now, endlessly generated.