You usually cannot tell for certain from a product ad’s pixels alone. Check the ad’s information panel and any image provenance or watermark signals first; then inspect visual details and verify the product against independent sources. Most importantly, whether AI helped make an image, whether the pictured product is genuine, and whether an advertised claim is true are three separate questions.
Start with the ad’s information panel
Open the ad’s information or transparency controls and look for a disclosure about AI creation or editing. Google says AI transparency details may appear after a click in My Ad Center, supported by machine-readable SynthID and C2PA information. Check the specific ad rather than assuming every platform exposes the same labels or covers every advertiser workflow. Google’s explanation of ad transparency in My Ad Center describes the available information.
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A label is evidence about the asset or the advertiser’s disclosure; it is not a certification that the product, its quality, or the scene shown is genuine.
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Look for Content Credentials
If you can obtain the original image file, inspect it for C2PA Content Credentials. These records can document an image’s creation and editing history. Adobe says its generative-AI workflow can record the tool and model, whether generation or editing occurred, the date and time, and an identifier; authenticity tools can be used to inspect credentials. See Adobe’s Content Credentials documentation.
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Credentials are a history record, not a universal AI detector or proof that the pictured product and scene are truthful. Google likewise cautions that Content Credentials do not establish that an image is definitely AI-generated; the recorded history and content still need to be assessed. Credentials may also be missing, unsupported, or absent from a copy of an image, so finding none does not prove conventional photography.
Use a watermark check only for the signals it supports
Google Gemini can check images for SynthID and compatible Content Credentials. Google says that if SynthID is detected, “all or part of the image or video was created or edited by Google’s AI models.” A positive result is therefore meaningful evidence of Google AI involvement in at least part of the image. A negative result only means the check did not recognize a supported signal; it does not rule out image generation or editing by other providers. See Google’s instructions for verifying AI-generated images, videos, and audio.
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OpenAI’s verification page also describes checking supported C2PA and SynthID signals, rather than detecting every provider’s AI output. Before relying on any checker, see which signals it supports and what a negative result actually means: OpenAI’s explanation of C2PA in ChatGPT images.
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Inspect visual details—but treat them as clues
Zoom in on small or complex details, then check whether they remain coherent across the whole image. Areas worth examining include:
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- Text, logos, labels, and fine print.
- Edges where the product meets a hand, surface, or background.
- Repeated components, such as buttons, seams, handles, or openings.
- Reflections, shadows, perspective, and the apparent direction of light.
- Material texture and the geometry of the object.
Artifacts, inconsistencies, or implausible details can justify further checking, but none is a dependable stand-alone test. Generators, editing, compression, and ordinary photographic defects all affect what an image looks like. A malformed logo or odd reflection may raise suspicion; it does not prove that AI made the image. The academic guide on detecting AI-generated images describes visual cues while noting how difficult provenance can be to establish from pixels alone.
Verify the product separately from the image
Compare the ad with multiple independent views of the product, the manufacturer’s specifications, packaging, and seller information. For a consequential claim—such as a feature, ingredient, included accessory, or promised result—look for evidence beyond the hero image.
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An image can have a documented history and still misrepresent what is for sale. Conversely, an AI-assisted image does not by itself prove that the product or an advertised feature is fake. Provenance tools address image history; product information and evidence address the offer.
What the different signals can—and cannot—tell you
| Signal | What it can indicate | What it does not establish |
|---|---|---|
| Ad-platform AI disclosure | The platform or advertiser has disclosed AI involvement or related transparency information for that ad. | Product quality, product identity, or the truth of a product claim. |
| C2PA Content Credentials | A recorded creation or editing history, which may identify a tool or operation. | A universal AI verdict or proof that the depicted scene is true. |
| SynthID result | A positive supported result indicates Google AI creation or editing of at least part of the image, according to Google. | A negative result does not rule out other AI systems or unsupported signals. |
| Visual inspection | Possible inconsistencies that merit closer investigation. | Reliable proof of AI generation on its own. |
| Independent product information | Evidence to assess product identity, specifications, and advertised features. | By itself, the image’s creation history. |
How much should an AI label affect your trust?
Disclosure does not have one universal effect on shoppers. A 2026 green-advertising study by Jingwen Li, Lixiao Geng, and Yaping Chang reported that, across three experiments with 632 participants, disclosing an AI-generated image rather than AI-generated copy increased brand attitudes and purchase intentions under the conditions studied. A separate 2026 e-commerce experiment reported that 72.8% of participants correctly identified the product image as AI-generated in its AI-label condition; that is a study-specific result, not a general shopper detection rate. The studies do not establish that labels always increase or decrease trust or sales across products and audiences. See the studies at Journal of Business Research and Electronic Commerce Research and Applications.
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Disclosure rules also depend on jurisdiction and context. For the United States, the FTC’s general endorsement guidance says disclosures should account for how long they are visible, competing text, size, and contrast; it does not prescribe exact wording. That guidance is not a complete statement of AI-specific advertising law. See the FTC’s Endorsement Guides FAQ.
Quick Recap
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