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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. Cropping, rotating, and similar conventional adjustments are not automatically generative-AI edits.
The distinction describes how the image was made, not whether the depicted event is true. Provenance records such as Content Credentials can document a recorded creation or editing history, but they cannot prove that a scene happened as shown.
AI-generated and AI-edited are different process claims
Use “AI-generated image” or “AI-generated photography” when a generative model creates the image or its visual content from a prompt, reference, or other input, without a camera photograph serving as the principal source. The model’s output is the image-making event.
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Use “photograph edited with generative AI” or “AI-edited photography” when a camera photograph remains the source but a generative tool changes what viewers see. The tool may synthesize replacement pixels, extend the frame, remove an object, or insert a new subject. Google’s Magic Eraser is an example: removing an object can be an AI edit when the software creates new pixels to fill the area.
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These labels should describe the operation you know occurred. Calling every image touched by an AI-enabled app “AI-generated” overstates the process, while calling a heavily altered photograph merely “retouched” hides a material change.
Generation is not a truth judgment
An AI-generated image can depict a real person, a fictional scene, or a synthetic mixture. Conversely, a camera photograph can be misleading through staging, cropping, captioning, or ordinary manipulation. “Generated” and “edited” identify production history; they do not answer whether the event, person, or location is authentic.
Mixed and composited images need a precise description
A composition can contain photographed elements and generated elements—for example, a real product photographed in a studio with a generative background. Call it a mixed or composited image and state which parts were generated when that information is available. A single yes/no label cannot describe every contribution accurately.
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| What happened | Useful description | Important qualification |
|---|---|---|
| A model created 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 include a generative-model output identified as trainedAlgorithmicMedia. |
| A camera photograph was retained, but a generative tool created, removed, or replaced visual content. | Photograph edited with generative AI; AI-edited photography. | Name the material operation, such as object removal, replacement, or generative expansion. |
| The image was cropped, rotated, resized, 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 AI assistance. |
| Photographed and generated elements were combined. | Mixed or composited image; identify the AI contribution. | C2PA can record multiple origins and media composition when participating tools preserve that information. |
| A participating tool supplied a valid provenance record. | “The credential records…” or “The file’s provenance indicates…” | A credential documents a signed tool record, not the truth of the depicted subject. |
| No compatible credential or AI label appears. | “No compatible credential was found,” if that is what you checked. | Do not infer that the image is not AI-generated. Records can be unsupported, stripped, or never created. |
Why the operation matters more than the word “AI”
Many image workflows contain both ordinary software and generative features. A phone may apply automatic exposure correction, a photographer may crop a frame, and an editor may then use a generative fill tool. Calling the entire file “AI-generated” erases those distinctions. A more informative caption might say: “Camera photograph, cropped and expanded with generative AI on the right edge.”
Conversely, a model can be used for a change that looks small but is still generative. Removing a passer-by and synthesizing the pavement behind them changes visual content. The relevant question is whether the tool created new image material, not whether the result looks realistic or whether the button was marketed as an enhancement.
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Editorial teams should write a short policy that defines their boundary. One defensible policy is to disclose any generative operation that creates, removes, replaces, or materially alters pixels, while listing ordinary crop, rotate, color, and exposure adjustments separately when they affect interpretation. If a client, newsroom, contest, or platform uses a broader rule, follow that rule and name it rather than presenting the boundary as universal.
What Content Credentials and C2PA can show
C2PA Specification 2.4 defines Content Credentials as manifests that represent provenance data: information about an asset’s origins and recorded actions. A manifest can include a creation action for an asset made de novo, or an opened action for an existing asset opened for editing. It can also record ingredients, software, actors, signatures, and later operations, depending on what the participating tool supplies.
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A credential is evidence of a recorded chain, not a lie detector
Google’s documentation states that a valid Content Credential confirms how a digital file was made or edited; it does not confirm the content’s truth or reality. A signed history may be accurate within its scope while the scene is staged, mislabeled, or taken out of context. It also cannot guarantee that every earlier action was recorded.
The practical wording is therefore “the credential records” or “the file’s provenance indicates,” not “the credential proves this event happened.” Keep the process claim and the factual claim separate in captions, investigations, and image credits.
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How Google’s labels distinguish certainty
Google Photos describes several provenance outcomes in its “How this was made” information for supported files. An Edited with AI tools marker is used for certain generative edits, such as Magic Eraser, where new pixels are created. A May have been edited by AI tools marker is weaker: it can appear when a third-party credential is present but does not specify whether AI powered the edit. Google also describes media composition and non-AI edits in supported histories.
Those labels should not be collapsed into one category. “Edited with AI tools” is a recorded, stronger process statement. “May have been edited by AI tools” reports uncertainty in the available credential. A conventional crop or rotate entry is not equivalent to either label.
Google says Content Credentials are not available for every photo or video, and its instructions describe the “How this was made” section in the mobile app rather than Google Photos on the web. Product support and rollout can change. Google’s May 19, 2026 announcement described expanding inspection features across Search, Gemini, Chrome, Pixel, and Cloud, so verify the current interface before publishing device-specific instructions: Google’s announcement.
How to inspect an image step by step
- Start with the best original. Use the camera file or the highest-quality export available. A screenshot, social-platform download, or re-saved copy may have lost the original manifest or changed the file.
- Open a compatible viewer. In Google Photos mobile, look for the image’s “How this was made” information when it is offered. Other participating applications may expose Content Credentials in their own interfaces. Google’s documentation notes that credentials are not available for every image and are not available in Google Photos Web.
- Read the exact wording. Record whether the interface says “Edited with AI tools,” “May have been edited by AI tools,” a non-AI operation such as crop or rotate, or a composition/origin entry. Do not paraphrase a qualified label as a definite one.
- Inspect the recorded source and actions. Note the named app or device, creation or editing action, ingredients, and signature status when shown. A record may identify only the last participating tool rather than the complete history.
- Check the file’s handling history. Ask whether it was exported, uploaded, downloaded, screenshotted, or passed through software that strips metadata. A missing record after those steps says little about the original.
- Report the limitation. If no compatible record appears, write “No compatible credential was found in the copy examined.” Do not write “not AI” unless you have independent evidence of the workflow.
- Separate process from subject verification. Use captions, witnesses, timestamps, location data, reverse-image research, or other evidence to assess what the image depicts. Provenance alone cannot establish the event’s truth.
What a missing credential does—and does not—mean
Content Credentials depend on participating devices and applications. An original tool may never create a record; a later application may not preserve it; and a platform may transform the image during upload. Screenshots and many re-saving or export operations can remove the manifest. For that reason, absence is an availability result, not a negative detection result.
Likewise, the presence of a credential does not guarantee that the chain begins at the camera or that every modification is included. Treat the manifest as a signed account of recorded actions. Check who supplied it, whether the chain validates, and what remains outside its scope.
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Visual “AI detectors” should be treated cautiously. Pixel-level guesses can be useful leads, but they are not a categorical verdict about process and should not override a documented, qualified history without corroboration. A detector also cannot tell you whether a camera image was later composited, mislabeled, or taken out of context.
How to compare two images or two provenance claims
When two files make competing claims, compare their evidence rather than their apparent realism. Use the following checklist:
| Question | What to examine |
|---|---|
| Was a camera capture or generative creation recorded? | Look for a creation action, camera origin, or a manifest ingredient identifying the source. |
| Which operation changed visual content? | Identify object removal, replacement, expansion, compositing, crop, color work, or other recorded actions. |
| How certain is the AI claim? | Distinguish a definite “Edited with AI tools” statement from “May have been edited by AI tools.” |
| Who supplied and signed the information? | Check the named device, application, actor, and whether the credential chain validates. |
| Is this the original or an altered copy? | Prefer the source file and note exports, screenshots, platform processing, or re-encoding. |
| What remains unknown? | List unrecorded earlier actions, unsupported tools, missing ingredients, and uncertainty about the depicted event. |
Do not rank one image as more authentic merely because it looks less synthetic. Real photographs can contain unusual lighting or lens artifacts, and generated images can look convincing. Process evidence and independent event evidence answer different questions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Examples that make the boundary clear
A prompt-created landscape
A model produces a mountain scene from the instruction “misty valley at sunrise.” No camera frame supplies the principal image. Label it AI-generated, even if the model used a reference photograph for color or composition; disclose the reference and its role if known.
Removing a person from a street photo
The camera photograph remains, but a generative eraser reconstructs buildings and pavement where the person stood. Label it a photograph edited with generative AI and describe the removal if it affects interpretation.
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Cropping and rotating a press photograph
The editor changes framing and orientation without synthesizing pixels. Describe it as a conventionally edited photograph. If the crop hides material context, disclose the crop for editorial reasons, but do not call it AI-generated solely because software performed it.
A product photo with a synthetic background
The product was photographed; the studio backdrop was generated. Call the result a mixed or composited image and identify the generated background. Saying simply “AI image” would conceal that the product itself was camera-captured.
Disclosure, archives, and editorial workflow
Keep the original camera file, the first generated output, and each significant export when your work requires an auditable record. Preserve manifests when your software supports them, and record the application, operation, date, and operator in the asset-management system. If a platform strips credentials, retain the original separately and note that the published copy no longer carries the record.
Use a caption or credit that matches the evidence: “AI-generated image,” “Photograph edited with generative AI to remove a person,” or “Mixed image: photographed product with generated background.” If you do not know the exact operation, say what is established and what is unknown instead of guessing.
Disclosure obligations can vary by jurisdiction, industry, contest, employer, and platform. The sources cited here explain provenance and product labels; they do not establish a universal legal rule. Consult the applicable law and your organization’s editorial policy before treating a disclosure as legally required.
What current provenance systems can realistically deliver
Content Credentials are most useful when the file stays in a compatible workflow from capture or generation through publication. They can make a recorded chain visible and can distinguish known AI edits from conventional operations. They are not a universal detector: unsupported tools may create no record, and a valid record can cover only the actions that participating software reported.
Google reported on May 19, 2026 that SynthID had watermarked more than 100 billion images and videos and 60,000 years of audio, and that Gemini image, video, and audio verification had been used 50 million times globally. Those are Google-reported usage figures, not independent measurements of detection accuracy or coverage. They indicate the scale of one provider’s systems, not proof that every AI image can be identified.
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The reliable conclusion for readers is narrow but useful: classify the operation, inspect the strongest available provenance, quote labels at their actual certainty, and keep truth-of-the-scene verification separate. That method remains valid even when a file has no credential or when several tools contributed to the final image.
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