Amazon’s AI Image Rules for Sellers in September 2026
As of 25 September 2026, Amazon requires sellers to disclose or label AI-generated people appearing in product listing images, according to reporting from Quartz, CNBC, and Forbes. The requirement is narrower than a general ban on artificial intelligence imagery. It does not automatically prevent Amazon sellers from using AI to create backgrounds, render non-human products, improve lighting, resize an image, or produce fully synthetic scenes, provided the result accurately represents what the customer will receive. The disclosure requirement is separate from Amazon’s existing rules about product accuracy, misleading images, image quality, and prohibited content. A label does not make an inaccurate product image acceptable.
Also worth reading: How can an e‑commerce brand scale high‑quality AI‑generated product imagery while keeping production costs under 15 % of total marketing spend in 2026? · What is the complete C2PA product image workflow for AI-generated visuals? · how to spot AI-generated romance scam images?
The practical distinction is between AI-generated people and other AI-assisted image work. A realistic person presented as a model, customer, reviewer, athlete, doctor, or brand spokesperson can create an expectation of human participation that may not exist. By contrast, a generated studio background or a corrected reflection may not require the same people-focused disclosure under the reported policy. That distinction may sound technical, but sellers should describe the actual production method rather than rely on assumptions about what counts as a minor edit. AI-generated human figures, realistic hands and faces, virtual try-on scenes, and composite lifestyle images deserve closer review than ordinary product cutouts.
Amazon’s policy notice and the interface available in the seller account are the controlling sources for an individual account. Reports describe the broad requirement but do not provide a reliable, permanent list covering every category, country, image type, and disclosure placement. Sellers should therefore avoid claiming that a watermark, filename, or image-generation prompt always satisfies Amazon. The safe approach is to use the disclosure option Amazon provides, confirm the wording shown to shoppers, and retain evidence of how each image was made. The New York legal developments cited in the research context helped prompt attention to synthetic people, but an Amazon marketplace policy should not be described as identical to every provision of a state law.
For an AI Product Images operation, compliance should be built into production before upload, rather than treated as a final visual check. The seller remains responsible for confirming that the depicted product, package, quantity, size, color, included components, and use case are accurate. AI can invent a logo, alter a label, add an accessory, or place a product in an impossible setting. Disclosure addresses one issue; it does not address these other failures. The correct response is usually to regenerate or retouch the image, verify it against the actual item, and then submit it through the required channel.
Why Amazon Introduced the Labeling Requirement
The main reason is to prevent buyers from believing that a synthetic person has used, endorsed, reviewed, or appeared with a product when no such involvement occurred. A familiar face can carry implied statements: a parent appears to recommend a stroller, an athlete seems to have tested sportswear, or a professional-looking model appears to be wearing the garment. If the person is fictional, the viewer may still interpret the scene as a real demonstration. Labeling creates a visible distinction between advertising imagery and a genuine human endorsement or customer experience.
Amazon’s concern also fits its wider responsibility to reduce deceptive content in a marketplace where images directly affect purchasing decisions. Product pages are not simply galleries; they are representations used to make buying decisions. Synthetic media can make an ordinary product appear premium, clinically tested, environmentally certified, or effective in a way the seller cannot support. Amazon has an incentive to preserve trust in seller content, especially when millions of listings may use polished photography of different quality. A disclosure rule allows some creative production while making the synthetic origin more apparent.
The policy should not be confused with a general requirement to label every image produced with computational tools. The reported Amazon action focuses on AI-generated people, while separate laws may address synthetic media more broadly. For example, European AI Act obligations and state rules can create additional duties depending on the content, audience, and transaction. A seller may therefore face two overlapping questions: does Amazon require disclosure, and does another law or platform rule require additional information? When those answers differ, applying the stricter requirement is usually the more defensible approach.
There is also a practical enforcement reason. Without a consistent disclosure mechanism, Amazon cannot easily distinguish deliberate deception from a seller who used AI under incorrect assumptions. Requiring a declaration provides a record that can be reviewed when images are questioned. This does not mean a disclosure makes enforcement unnecessary. Amazon can still inspect whether the product itself is represented accurately, whether the image belongs to the listing, and whether it follows category-specific visual standards. Disclosure is an additional control, not a safe harbor.
Main Images, Secondary Images, and Ordinary Product Standards
Amazon’s existing image rules remain at least as important as the new people-focused labeling requirement. For most categories, a main product image must use a pure white background, show only the item being sold unless additional included contents are required, and avoid promotional text, borders, watermarks, and unrelated props. The familiar U.S. main-image standard calls for a longest side of at least 1,600 pixels for useful zooming, with the product generally filling about 85% of the frame. Category-specific rules can differ, so sellers should check the requirements for apparel, jewelry, media, beauty, collectibles, and other categories rather than apply one checklist to every listing.
A labeling mark placed directly over the main image may conflict with the rule against text or watermarks. That creates an important operational question: should the image be changed, the disclosure added through Seller Central, or the synthetic human placed only in a secondary image? Sellers should not paste a homemade “AI” badge onto a main image unless Amazon specifically instructs them to do so. If the required label is shown as a separate disclosure, adding another watermark can still reduce image quality or make the asset look promotional. The cleanest process is to follow the interface Amazon provides and test the resulting listing rather than guessing at placement.
AI enhancement and AI generation should be documented separately. Upscaling a real photograph, correcting exposure, removing dust, and extending an existing background may not add new factual claims. Generating a new model, changing body proportions, swapping fabric texture, or placing the item in a synthetic environment can change the product representation. An enhancement that turns blue fabric purple or removes a visible scratch is not harmless presentation work if the customer receives a materially different item. Sellers should keep the original photograph beside the enhanced version so reviewers can compare texture, color, seams, logos, and package details.
The main image also has a different function from a secondary lifestyle image. A main image usually helps the buyer identify the exact item, while a secondary image may explain dimensions, materials, use, packaging, or a comparison. A generated person is more likely to appear in the secondary set, but a seller cannot assume that moving an image out of the main-image position removes the disclosure obligation. Any realistic AI-generated human shown as a product user or spokesperson should be treated as disclosed content regardless of its position, at least until Amazon provides more specific guidance.
Comparing Photographs, AI-Assisted Images, and Synthetic Images
The best alternative is not always a real photograph, but the production method must match the purpose of the asset. A genuine photograph can be less consistent between listings, while AI can make a large catalog visually uniform at the cost of accuracy risk. Conventional retouching and 3D rendering can also reduce the need for synthetic human models, although neither method is automatically free of image-policy obligations. The table below compares the main options for a seller building an AI Product Images workflow.
| Feature | Genuine photograph | AI-assisted or conventional retouched image | Fully AI-generated image |
|---|---|---|---|
| Product accuracy | Strongest when the real item is photographed and checked | Strong when changes are limited and compared with the original | Higher risk of invented details, altered text, or impossible features |
| Human representation | A real model or documented person may be used | Retouching should not change the real person’s identity or implied endorsement | A realistic synthetic person may require Amazon disclosure under the reported policy |
| Main-image suitability | Usually good when cropped, lit, and background-cleaned | Often good for cleanup, resizing, and background correction | Suitable only when category rules, accuracy, and disclosure are handled correctly |
| Production cost | Often highest per unique setup, but many sellers own a camera | Typically $10–$30 monthly for software, plus labor for higher-end retouching | Often $10–$30 monthly for generation tools, with additional review time |
| Documentation needed | Original file, release information where relevant, and product photos | Original file plus a record of every material edit | Prompt, source, model, date, disclosure record, and product-verification evidence |
| Main failure mode | Poor lighting, cropping, or inconsistent backgrounds | Overretouching that changes color or product details | Convincing but false features, labels, accessories, or human presence |
AI-assisted production sits between those extremes and can work well for catalog cleanup. A seller might remove a cable from the background, correct a white balance problem, increase resolution, or place a real cutout product into a controlled background. None of these actions guarantees compliance, because the tool may alter the product while trying to improve the scene. The safer test is whether a buyer could be misled about a physical attribute. If the answer is yes, the edit needs correction even if the intent was to make the listing look better.
Fully generated images can save time for concept work, backgrounds, and non-human renderings, but they carry the highest review burden. A seller should compare every generated asset with reference photographs, inventory records, and packaging files. A photorealistic CGI person should also be treated cautiously: if a rule is written around AI-generated people, a different rendering method may not remove the underlying deception concern. When the distinction is unclear, support should receive a specific description and example image rather than the word “artistic.”
A Practical Compliance Workflow for AI Product Images
Start by classifying each asset in 3 ways: whether a real product was photographed, whether a human was altered or generated, and whether the final image adds a factual claim. Create a production record containing the source photograph, generation tool or retouching service, model version where available, date, prompt or edit instructions, reviewer, and disclosure decision. A useful classification can be labeled “real product photograph,” “AI-assisted,” or “synthetic scene,” but the description should explain what actually changed. Files named “final-final-2.jpg” provide almost no protection if a dispute occurs six months later.
Create a clean product master before adding context. Use a real or verified product image, correct perspective, preserve the true color, retain every logo and included component, and remove unsupported accessories. Check the image at 100% magnification and at mobile-list size, because a misspelled logo or synthetic button may disappear on a small screen while remaining obvious on a large monitor. For visual accuracy, compare the rendered product with at least 2 independent references when possible: the physical item and an unedited photograph. This is especially important for text, jewelry, watches, printed labels, cosmetics, and products whose color influences the purchase.
Test the disclosure route before generating an entire catalog. Create 1 sample image with a realistic person, upload it through the current Seller Central flow, and record exactly what Amazon asks the seller to confirm. Check whether the disclosure is entered in a field, displayed as a label, or requested through another mechanism. Do not assume that a watermark embedded in the JPEG will be recognized. If the interface is ambiguous, open a support case with the image, product category, production method, and a direct question about labeling rather than describing the file simply as “AI art.”
Review the image claim by claim. Ask whether the product quantity, dimensions, color, material, package contents, compatibility, and performance are all supported. Remove any visual statement that could imply certification, clinical testing, professional endorsement, sustainability credentials, or a guaranteed result unless the seller can document it. A label saying “AI-generated person” does not validate a false claim about what the product does. One inaccurate claim can cause more damage than the synthetic visual itself, particularly in health, beauty, finance-adjacent, children’s, and safety-related categories.
After upload, inspect the live listing rather than only the source file. Confirm that the image is not cropped incorrectly, that the main image meets the category standard, that the synthetic human is disclosed, and that the product remains identifiable. Save the final listing screenshot, submission confirmation, and disclosure evidence for at least 12 months, and longer if the item is regulated, seasonal, or repeatedly edited. That retention period is a prudent operating recommendation, not a claim that Amazon publishes one universal recordkeeping rule. If a complaint arrives, the seller should be able to show what was submitted and why it was believed accurate.
Common Mistakes That Create Compliance Problems
One common mistake is treating the label as permission to use any image. A disclosure does not correct a product that was digitally reshaped, a logo rewritten by a diffusion model, or a package with a nonexistent ingredient. Another mistake is assuming that moving the image to the second slot makes it a lifestyle image rather than regulated product content. Amazon evaluates the representation of the product, not just the image’s position. Sellers should apply the same factual checks to primary and secondary assets, with extra attention to realistic people and implied endorsements.
The second major mistake is failing to distinguish AI assistance from AI generation. A seller may believe that upscaling or background removal is identical to creating a new model, but the tool can still modify texture, edges, color, and package text. Conversely, a seller may call a completely synthetic image “just a mockup” and omit disclosure. The production process should be recorded at a level that allows another person to understand what was original and what was created. If that distinction cannot be explained clearly, the asset is not ready for upload.
A third mistake is adding a homemade watermark without checking the image placement rules. A visible badge can clutter the product, obscure a required feature, and conflict with restrictions on text in the main image. It may also fail to feed the disclosure into Amazon’s own records. Sellers should not use an embedded label merely to appear compliant while leaving the formal requirement unmet. The preferred solution is the native disclosure route, supported by a clear image that does not need a large banner.
A fourth mistake is trusting the prompt or the vendor’s statement that an image is “ready for Amazon.” Generation platforms are not regulatory reviewers and usually do not know the exact product, category, listing history, or seller account. Their commercial claims may be designed to simplify production, not verify policy. The human reviewer must compare the result with the item being sold and check current category guidance. This extra review may feel slower than generating 100 variations in a few minutes, but one bad batch can affect an entire catalog if the same error is repeated.
When to Act and What the Process May Cost
Sellers should act before publishing new AI imagery and review existing assets when the policy is communicated, when a listing receives a complaint, or when a product is materially updated. High-risk first targets are apparel shown on synthetic models, beauty products shown on faces or bodies, children’s products, health-related items, jewelry, and any listing that implies professional approval. If Amazon sends a notice, treat it as time-sensitive even if no suspension date is visible in the message. A practical internal target is to classify affected images within 24 hours and complete correction within 48–72 hours, adjusting that window for the severity of the notice.
Ordinary product photography can be produced at no software cost if the seller already owns the equipment and lighting. Amazon’s long-standing U.S. plan structure has commonly included an Individual plan at about $0.99 per item and a Professional plan at about $39.99 per month, but fees, image allowances, and regional terms can change, so the seller should verify the 25 September 2026 account page rather than budget from an old article. Image-upload fees are also account- and region-specific. A seller should separate the cost of the Amazon selling plan from the cost of generating, retouching, reviewing, and documenting assets.
Many cloud image generators and editing suites use subscriptions in the approximate range of $10–$30 per month for individual access, while higher-volume or enterprise services can cost more. Conventional freelance retouching may range from roughly $25 to $150 per image depending on complexity, and a full human catalog review can take several hours or days. A small seller may therefore spend less by replacing synthetic models with real flat-lay or white-background photographs than by paying for generation and manual accuracy checks. The lowest-cost method is the one that produces accurate assets quickly enough to avoid takedowns, repeated edits, and support work.
Automation can help with file naming, duplicate detection, image quality checks, and records, but it cannot decide the final compliance question without reliable product references. Amazon’s cloud and AI services may support internal governance systems, yet an automated score cannot confirm whether a garment’s color or a package’s label is authentic. Budget time for a human approval step on every new template and a sampled recheck after the model or software changes. The cost is modest compared with reconstructing a catalog after a broad image-removal request.
What Enforcement and Documentation Should Expect
Amazon’s public reporting on the new labeling requirement does not provide a complete penalty matrix stating exactly how many strikes lead to suspension. That absence matters because sellers often quote unsourced claims that a single synthetic image automatically causes removal. In practice, the consequences can range from a request to correct the disclosure to image removal, offer suppression, listing suspension, or account review, depending on the severity, history, and nature of the violation. A transparent record helps the seller explain that the image was disclosed, while an inaccurate image still requires correction.
Documentation should connect 4 elements: the product reference, the production method, the disclosure action, and the final Amazon listing. For example, the seller may retain the original photograph showing the package, the generated lifestyle image, the prompt and tool version, a reviewer note confirming the logo and color, and a screenshot showing Amazon’s disclosure. The record should be updated whenever the asset is regenerated. Keeping the prompt without the output is not enough, just as keeping the output without the prompt leaves no way to distinguish a real human from a generated one.
Compliance is an ongoing process because both the technology and the marketplace rules can change. Generation models improve at producing realistic hands, text, reflections, and human faces, which makes visual detection harder and raises the expected quality of review. Sellers should check Seller Central notices at least monthly, revisit category rules when entering a new category, and recheck disclosure wording whenever Amazon updates the interface. A template approved in 2026 may be handled differently in 2027, particularly if Amazon changes the label, permitted placement, or definition of a generated person.
The most defensible position is neither “all AI is forbidden” nor “a label fixes everything.” Amazon sellers can use AI product imagery when the product is represented accurately, generated people are disclosed under the current requirement, and every listing follows ordinary image standards. The seller should also know when a real photograph, conventional retouching, or a 3D non-human render would reduce risk. As of 25 September 2026, the reported people-labeling rule is a reason to review production methods, not a reason to remove every generated asset. It rewards sellers who can show what was created, why it is truthful, and where customers can see the disclosure.