AI-generated photography is an image whose visual content was created by a generative model rather than captured as the principal image by a camera. AI-edited photography starts with a photograph but uses generative AI to create, remove or replace visual content. A crop, rotation or ordinary tonal adjustment is not automatically a generative-AI edit.
The label describes how the image was made, not whether the scene is true. Provenance records can document a tool-supplied creation or editing history, but they cannot prove that the depicted event happened.
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The process-based definition
The most useful boundary is the operation performed on the image, not the fact that software or machine learning was involved somewhere in the workflow. Ask two questions:
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- Did a generative model create new visual content or alter existing content?
If a model creates the image from a prompt, sketch, reference image or other input without a camera photograph as the principal source, describe the result as an AI-generated image or, when the context is photographic, AI-generated photography. If a camera photograph remains the base but a generative tool adds, removes or replaces pixels, describe it as a photograph edited with generative AI or AI-edited photography.
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This wording is more precise than calling every image made with an AI-enabled app “AI-generated.” It tells readers which part of the visual content came from a model and which part came from a camera.
What counts—and what does not
| What happened | Useful description | Important qualification |
|---|---|---|
| A generative model made the image from a prompt or other input, with no camera photograph as the principal source. | AI-generated image or AI-generated photography | The C2PA standard treats this as a creation action; its examples use a generative algorithmic-media source. |
| A camera photograph was retained, but a generative tool created, removed or replaced visual content. | Photograph edited with generative AI; AI-edited photography | Google gives Magic Eraser as an example when new pixels are created. |
| The image was cropped, rotated or adjusted without generative creation of pixels. | Conventionally edited photograph | Google’s documented history treats crop and rotate as non-AI edits. Other editorial policies may disclose additional assisted operations. |
| Photographed and generated elements were combined. | Mixed or composited image; identify the AI contribution | A single binary label can hide multiple origins, so describe which elements were generated and which were photographed. |
| A participating tool supplied a valid provenance record. | “The credential records …” or “The file’s provenance indicates …” | The record documents what the participating tool signed; it does not prove that every earlier action is known. |
| No AI label or compatible credential appears. | “No compatible credential was found” if that is what you checked | Absence is not proof that no AI was used. Records can be unsupported, stripped or unavailable. |
Why a normal edit is different from a generative edit
Traditional image operations change the arrangement or appearance of pixels that already exist. Cropping removes part of the frame; rotating changes its orientation; exposure, color and contrast adjustments change values in existing pixels. They can be editorially significant, but they do not necessarily introduce generated visual content.
Generative editing changes the evidentiary situation because the software synthesizes pixels that were not present in the source. Removing a person and reconstructing the background, extending a landscape beyond the original frame, replacing a sky or inserting an object are examples of AI edits when a generative model performs the reconstruction. Google’s own labeling example is Magic Eraser: when it creates new pixels, Google classifies the operation as an AI edit.
Do not turn this practical boundary into a universal law. A newsroom, contest, stock library or platform may choose to disclose additional AI-assisted operations, including tools that make selections, denoise or retouch without generative replacement. State the policy you are applying and name the operation rather than implying that every machine-learning feature has the same status.
AI-generated does not mean “fake,” and a camera capture does not mean “true”
Generation and truth are separate claims. A completely synthetic image can accurately illustrate an imaginary scene, while an untouched photograph can be misleading because of framing, timing, staging or a false caption. Even a genuine camera exposure records only what the camera saw from one position and moment.
Content Credentials and similar records are therefore evidence about a file’s recorded history, not an authenticity verdict. Google’s documentation states that a valid credential confirms how a digital file was made or edited, but does not confirm the content’s truth or reality. A credential may show that a camera created a file and that no subsequent generative action was recorded; it cannot establish that the event depicted really occurred as described.
How Content Credentials and C2PA record provenance
C2PA Specification 2.4 defines manifests that represent an asset’s provenance. In practical terms, a manifest can record an action, the input asset, the tool or actor that supplied the information and a cryptographic signature used to validate the record.
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Creation and editing actions
The specification distinguishes a creation action for an asset made de novo from an opened action for an existing asset opened for editing. That vocabulary maps neatly to the photography question: a generative model can be recorded as creating an image, while a camera file opened in an editor can carry a history of later operations. The standard’s examples include generative output and an existing photograph edited with operations such as crop and vignette.
Multiple origins and composites
C2PA can record media composition and multiple origins when the participating tools preserve that information. A composite might therefore contain a camera photograph as one input and generated elements as another. The right description is not simply “real” or “AI”; it is a statement such as “camera photograph with a generated background,” provided the record or production documentation supports that claim.
What a signature actually establishes
A valid signature establishes that the named tool or device supplied and signed the recorded information and that the manifest has not been altered in a way the validation process detects. It does not guarantee that the tool saw every earlier version, that an unrecorded editor did nothing later, or that the subject matter is factual.
Adobe’s generative-AI content-labeling documentation describes automatic C2PA metadata for qualifying workflows and the information those records can include. “Qualifying” matters: coverage depends on the application, feature and export path.
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Google Photos provides a useful example of how certainty can be expressed in user-facing language. Its help documentation describes categories for media composition, AI edits, possible AI edits and non-AI edits.
- Edited with AI tools: a generative AI edit is recorded, such as an edit that creates new pixels.
- May have been edited by AI tools: a third-party credential is present but does not specify whether AI powered the edit.
- Non-AI edits: documented operations such as crop and rotate are identified as ordinary edits.
- Media composition: the history can indicate how photographed and generated or otherwise separate media sources were combined.
These labels are deliberately different strengths of claim. Do not rewrite “may have been edited” as “was edited,” and do not treat “non-AI edits” as a universal statement about every operation ever performed on the file.
How to inspect an image’s AI history
Use the highest-quality original available. A screenshot or a file downloaded from a social platform may no longer contain the source manifest or may contain a transformed copy.
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- Preserve the original. Keep the camera or application export, its filename and any sidecar files before opening it in another editor or sending it through a platform.
- Open a compatible viewer. Google Photos describes a How this was made section in its mobile app for supported files. Its documentation says credentials are not available for every image and are not available on Google Photos Web.
- Read the exact wording. Record whether the viewer says “Edited with AI tools,” “May have been edited by AI tools,” a non-AI edit, media composition or no available credential.
- Inspect the recorded actions and signer. Note the stated source, the operation that changed content, the application or device that supplied the record and whether the credential validates.
- Report gaps explicitly. If the record is absent, unsupported or incomplete, say so. “No compatible credential was found in the inspected copy” is accurate; “not AI” is not.
- Separate process from subject matter. Even a valid history does not verify the event, location, identity or caption shown in the picture.
Google’s AI-label and Content Credentials guidance also warns that file handling can remove or alter records. Treat a credential as one line of evidence, not as a universal detector.
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Start with the production record: the photographer’s original RAW or JPEG, the editor’s project file, export logs and statements from the person who made the image. These sources can explain intent and operations even when a platform has stripped metadata.
Visual appearance alone is weak evidence. Anatomical oddities, inconsistent reflections or repeated textures may justify further checking, but they are not a categorical verdict. Conversely, a photorealistic synthetic image may contain no obvious visual tell. If you cannot establish the process, use qualified language such as “AI use could not be verified from the available file and records.”
Do not infer “no AI” from missing metadata. The original tool may never have created a credential, the format may not support it, or a screenshot, export, resize or platform upload may have removed it.
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Generative fill around a real subject
If a portrait was captured by a camera and generative fill expands the room or replaces the background, the base remains a photograph but the altered areas are AI-generated content. Identify the scope: “camera portrait with a generatively extended background” is more informative than “AI photo.”
Object removal
Removing a cable, person or sign with a model that reconstructs the background is an AI edit because the replacement pixels are generated. If a conventional clone or healing operation copies existing pixels without generative synthesis, follow the applicable editorial policy and describe the retouching precisely.
Reference-image workflows
A model may use a photograph as a reference while generating an entirely new image. Unless the camera file remains the principal visual source, call the result AI-generated and disclose the reference relationship if it matters to the audience.
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Collages and composites
When a project combines several photographs, illustrations and generated elements, list the origins that affect interpretation. A binary label hides whether the generated component is a minor texture, a replacement subject or the entire scene.
A practical comparison framework
When evaluating two images or two claims, compare the evidence in the same order:
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|---|---|
| What is the principal source? | Camera capture, generated output, or several inputs. |
| Which operation changed visual content? | Creation, insertion, removal, replacement, crop, color adjustment or another named action. |
| How certain is the AI claim? | Definitely recorded, only possibly indicated, or not established. |
| Who supplied the evidence? | Camera maker, application, editor, platform or the image creator’s own statement. |
| Is the file original and the chain valid? | Preserved source file, validated credential and known export path, or a transformed copy. |
| What remains unknown? | Unrecorded prior edits, missing manifests, unsupported tools and the truth of the depicted event. |
Do not rank the images by apparent realism. Realism is a property of appearance; generation and editing are claims about process.
Using the classification in publishing and attribution
Write the smallest accurate disclosure that answers the reader’s likely question. Examples include:
- “AI-generated image created from a text prompt; no camera photograph was used as the principal source.”
- “Photograph captured on camera; the sky was replaced with a generative tool.”
- “Camera photograph with conventional crop and rotation; no compatible AI credential was found in the supplied copy.”
- “Composite assembled from a camera photograph and a generated subject; the available credential records the composition.”
Keep the process statement separate from the caption’s factual claims. If the image illustrates a real event, verify that event independently through reporting, documents or eyewitness evidence. A provenance record cannot substitute for that work.
Common mistakes to avoid
- Calling every AI-assisted workflow AI-generated: name the operation and identify whether the camera image remains the principal source.
- Treating C2PA as a detector: the standard records provenance when participating tools create or maintain credentials; it cannot cover every image.
- Calling a clean credential proof of authenticity: it is a signed history within the scope of what was recorded.
- Calling missing metadata proof of no AI: records can be absent, unsupported or stripped during file handling.
- Flattening “may” into “did”: preserve the certainty level used by the credential or documentation.
- Making universal legal claims: disclosure duties vary by jurisdiction, contract, contest rules and platform policy. Check the rules that govern your specific publication.
Availability changes over time
Product labels and verification tools are still evolving. Google’s May 19, 2026 announcement, “Tools to understand how content was created and edited,” describes expanding inspection features across Search, Gemini, Chrome, Pixel and Cloud. Availability can differ by country, account, device, file type and rollout stage, so verify the current product documentation before publishing instructions tied to a particular app or device.
The bottom line
Use AI-generated when a generative model created the image’s visual content without a camera photograph as the principal source. Use AI-edited when a photograph remains the base but generative AI creates, removes or replaces visual content. Describe ordinary edits separately, identify mixed origins, preserve the exact certainty of any credential and never mistake a provenance record—or its absence—for proof that the depicted scene is true or false.
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