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Provenance

As explained in Introduction to Generative AI, Second Edition

Content provenance is the documented history of a digital work's origin, transformations, ownership, and associated authenticity evidence.

A real video can be dismissed as AI-generated, while a convincing synthetic one may be accepted as genuine. Content provenance addresses a different question from visual inspection: what recorded evidence shows where a digital work came from and how it changed? section 10.23

A provenance system records a work’s history using information such as metadata and cryptographic signatures. A signature helps others check whether the associated record has been altered. The goal is to make origin and edits inspectable, rather than infer them solely from flaws in an image, video, or text. section 6.9 section 4.4

A provenance record may help a viewer examine:

  • Creation details, such as when and where a photo or video was captured and which device was used. section 10.23
  • Subsequent edits and the record associated with them. section 10.23
  • Whether the source and integrity of a document can be verified through its recorded history. section 6.9

Provenance at capture

The Coalition for Content Provenance and Authenticity (C2PA) provides an open standard for tracing media origins. In 2025, Google announced that photos taken with the native camera app on its Pixel phones would include C2PA credentials. This is a step toward records viewers could inspect, not universal coverage: adoption across platforms and easy verification remain challenges. section 10.23

Provenance complements watermarking rather than replacing it. A watermark places a detectable signal in the content; provenance can instead trace an associated history through signatures and metadata. C2PA currently focuses on images and video, while comparable infrastructure for text is being explored. Neither approach is a complete answer to synthetic-media deception, and provenance’s value depends on broad support from creators, institutions, and platforms. section 6.9 section 6.12

The question also matters before publication. When data is collected for AI training without clarity about its origins, tracing what went into a system becomes harder. Provenance can support transparency about sources, but it does not by itself settle questions of consent, attribution, or appropriate use. section 3.18 section 6.9

How Introduction to Generative AI, Second Edition covers it

How deep it goes
Defines provenance through metadata, signatures, and audit trails, but stops short of explaining cryptographic implementation.
Examples
Uses C2PA capture and edit records and Google Pixel camera credentials to illustrate provenance in practice.
What it assumes
Introduces cryptographic signatures in plain language rather than assuming knowledge of how they work.
What you can do after
Evaluate/understand what a provenance record could verify, while recognizing limited adoption and the need for accessible verification.
How it connects
Connects provenance to watermarking, synthetic-media detection, cryptographic signatures, data governance, and AI training data.

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