AI-powered defect generation for instant refunds: a step-by-step pipeline

[Good Carder]

From fraudster to fraudsters. While you're carding cards and cashing out crypto, another army of fraudsters has been profiting from this "digital" scam for a year now — no hacking, no proxies, no checkers. They take a photo of a regular product, run it through a neural network, and get a photo of a "damaged" product. Then they send it to the seller and get a refund. The product stays with them. This is called AI refund fraud, and by 2026, it had become a veritable epidemic.

Forter, a fraud prevention platform, has named AI-generated damage claims the fastest-growing form of refund abuse. According to the company, 80% of retail fraud is now linked to AI, and one refund group has enriched itself by $800,000 in a month.

In this article, I'll break down the full AI refund fraud pipeline: what tools are used, how to generate realistic defects, how to integrate with Amazon's return policy, and how to avoid getting caught on minor issues. No fluff — just how it works right now.

Part 1. Why It Works: The Anatomy of the Returnless Refund Policy​

Before attacking, you need to understand what you're fighting against.

Amazon and many other platforms use a "Returnless Refund" policy. The idea is simple: for inexpensive items (usually under $75), it's cheaper to refund than to process the return, inspect the item, and repackage it. Amazon's algorithm makes its decision automatically based on three factors: the item's price, the customer's history, and the item's category.

This automation is our main loophole. The system trusts uploaded images. If you upload a "convincing" photo of a damaged item, the algorithm is almost guaranteed to approve the return. All you have to do is make the photo look real.

Why this works against sellers:

• Platforms trust images uploaded by buyers.
• Return systems are often automated, with decisions made within minutes.
• It's difficult for sellers to dispute returns, especially for items under $100.
• The cost of litigation often exceeds the value of the goods.

In 2026, the number of AI-based refund fraud cases grew so much that government agencies began to respond. China's National Anti-Fraud Center launched an "AI Content Identification" feature in its app, and researchers created FraudBench, a benchmark for detecting fraudulent AI-generated images. But while the system is being refined, a loophole remains open.

Part 2. Toolkit: From Midjourney to Local Models​

In 2026, the arsenal of available AI tools is impressive. You don't even need to be an expert — all you need is the ability to write prompts.

2.1. Popular AI generators for creating defects​

Tool	Type	Price	Peculiarity
Midjourney	Diffusion model	$10–30/month	Better quality, more features, but requires learning how to write prompts
Nano Banana	Diffusion model	Free / conditional	Simple interface, quick learning
Stable Diffusion	Open-source	For free	Full control, you can further train it on your own data
FROM 3	Diffusion model	$20/month	Good quality, strict content filters
Fooocus	Open-source	For free	Simplified Stable Diffusion interface

Reality 2026: Low entry barriers and low costs are the key factors that have made AI refund fraud a widespread phenomenon. Most fraudsters use free or shareware tools.

2.2. Local Models: Full Control Without Censorship​

If you don't want your prompts stored on Midjourney servers, use local models:

• Stable Diffusion with custom control networks (ControlNet).
• Fooocus is a simplified version of Stable Diffusion with an intuitive interface.
• ComfyUI is a node-based interface for professional work with diffusion models.

2.3. Artifact and Watermark Removal Tools​

The finished AI-generated defect often contains "digital seams" — inconsistencies in texture, color, or lighting. These need to be removed:

• Photoshop with AI functions (Generative Fill) - correction of artifacts.
• Remove.bg — removes the background if you need to insert a defect into another photo.
• Inpaint — completing the missing parts of an image.

Part 3. Step-by-step pipeline: from a regular product to a successful return​

3.1. Step 1. Photoshoot and source material preparation​

What you need to do:

1. Photograph the product in good lighting. Avoid shadows and glare, as they complicate image generation.
2. Take several photos from different angles. The more sources you take, the higher the quality of the final image.
3. Make sure the background is neutral — white or a solid color. This makes the AI's job easier.

A rookie mistake: using a poor-quality photo or one with a hand in the frame. AI generates the defects, but the "hand" remains intact — it's immediately obvious.

3.2. Step 2. Selecting the defect type​

Select which defect you want to "add":

Product type	Recommended defect	Why does this work?
Electronics	Cracks on the screen, scratches on the case	Easy to imitate, hard to refute
Cloth	Fabric tears, stains, loose threads	High variability, difficult to test
Food products	Mold, rot, damaged packaging	Often falls under Returnless Refund
Cosmetics	Cracks in the powder, broken bottles	Visually convincing
Books/paper products	Torn pages, stains, creases	Just imitate

According to research, low-value goods with a high risk of damage during transportation — clothing, cosmetics, and food — are most often attacked.

3.3. Step 3. Defect generation via AI​

Example prompt for Midjourney / Nano Banana:

Take this photo of a [product]. Add realistic damage: [type of damage]
on the [specific location]. The damage should look natural, with consistent
lighting and shadows. The rest of the product should remain unchanged.

A specific example for books:

Take this photo of a book cover. Add a coffee stain in the bottom right corner.
The stain should look natural, with slightly darker edges and a lighter center.
The text on the cover should remain readable. The lighting should be consistent
with the original photo.

For electronics:

Take this photo of a smartphone. Add a crack on the screen starting from the
top left corner and spreading diagonally. The crack should look like glass,
with realistic reflections and light refraction. The rest of the phone should
remain unchanged.

By 2026, AI tools will be able to accurately simulate various defects: fabric tears, glass cracks, impact marks, liquid stains, and even shipping damage.

3.4. Step 4. Post-processing and masking​

AI-generated images often have "digital seams" — inconsistencies in texture, color, or lighting.

Post-processing checklist:

• Remove AI watermarks. Some generators leave watermarks behind — they need to be removed.
• Fix artifacts. If the AI "ate" part of the product, draw it back.
• Adjust color and contrast. AI images often look "too perfect."
• Add noise. Real photos have grain.
• Check the shadows. The shadow from the "defect" should match the shadow from the product.

Important: Even with a perfect AI image, logical inconsistencies can still be detected. If you generate "mold" on fruit, make sure the mold matches the fruit type.

3.5. Step 5. Submitting a refund request​

Algorithm:

1. Log in to your Amazon (or other platform) account.
2. Go to your order history and select the target order.
3. Нажми «Return or replace items».
4. Select the reason: "Item defective" or "Damaged".
5. Upload the generated photo.
6. Confirm your request.

What to do if you are asked for additional information:

• Don't panic. Just upload another photo from a different angle.
• If you requested a video, decline the refund and try on a different account.
• Don't try to "prove" - if the system doesn't believe you, just back off.

Statistics: According to research by Riskified, approximately 1 in 4 dollars returned through return programs is fraudulent. Algorithms often fail to check images for AI-related fraud, especially for items under $100.

Part 4. Scaling: From Single Returns to Returns-as-a-Service​

In 2026, AI refund fraud is no longer the preserve of individuals. Fraudster groups offer "returns-as-a-service," taking a percentage of profits in exchange for assistance in obtaining fraudulent refunds.

How "Returns-as-a-Service" works:

1. The client sends a photo of the product to the group.
2. The group generates an AI image of the defect.
3. The customer sends a photo to the seller and receives a refund.
4. The group receives 30-50% of the refund amount.

Some fraudsters sell ready-made "AI refund kits" — prompts, templates, and even ready-made images for popular products. The price of such a kit is $288. One "trained" account can successfully process up to 30 refunds.

Part 5. Errors that burn out the circuit (and how to avoid them)​

5.1. Error: Leftover AI watermark​

Beginners often forget to remove watermarks or signatures from AI generators.
Solution: Always check your image for watermarks. Use watermark removal tools or crop the image so the watermark isn't in the frame.

5.2. Error: Texture and color mismatch​

AI often generates defects that look "too perfect" or have texture inconsistencies.
Solution: add noise, adjust contrast, and use post-processing in Photoshop. Real-life defects look "dirty" rather than "perfect."

5.3. Error: Mismatch between shadow and lighting​

The shadow of the defect should match the shadow of the product. AI often ignores this.
Solution: use ControlNet or manual shadow correction in Photoshop.

5.4. Error: Reusing the same image​

If you use the same photo for multiple returns, the system may recognize it.
Solution: generate a unique image for each return. Vary the angle, lighting, and defect type.

5.5. Error: A product that is too “perfect” against the background​

AI often generates a defect but leaves the rest of the product "perfect." This looks suspicious.
Solution: add minor "wear" or "stains" to the rest of the product. Make the entire product appear "used."

5.6. Error: No account history​

A new account that immediately requests a refund looks suspicious.
Solution: Use accounts with a purchase history. Make two or three small purchases before returning them.

Part 6. Checklist: How to Conduct an AI Refund Without Sleeping​

• Select an item priced between $20 and $75 (qualifies for Returnless Refund).
• Take a high-quality photo in good lighting.
• Generate a defect using Midjourney or Stable Diffusion.
• Remove AI watermarks and artifacts.
• Adjust color and contrast to match the actual photo.
• Add noise to simulate a real image.
• Use an account with purchase history.
• Request a refund with the reason "Damaged".
• Don't use the same image twice.
• If they ask for additional information, back off.

Summary​

AI-based refund fraud is a scheme that turns a regular order into a free product. You buy the product, generate an AI image of the defect, send it to the seller, and receive a refund. You keep the product.

In 2026, this scheme became widespread: the tools became accessible, and the Returnless Refund policy created ideal conditions. According to Forter, AI-generated damage claims have become the fastest-growing form of return abuse. Researchers created FraudBench, a benchmark for detecting such images, but AI detection currently lags behind AI generation.

The main risks are: a left-over watermark, texture and shadow mismatch, and an overly "perfect" product. Use aged accounts, unique images, and post-processing. In 2026, AI-based refunds remain one of the most effective schemes — until detection systems catch up with generation.

A quick one-line reminder:
"Buy a product for $50, generate "mold" in Midjourney, remove watermarks, add noise, send to the seller, and get a refund. You keep the product. 30 successful returns from one account = $1,500 profit. Just don't get caught out by the watermarks and inconsistent shadows."