Stopping Counterfeits in Your Catalog: How to Detect Fake Product Images in E-Commerce

The headline number was hard to miss. On March 27 2025 Amazon reported that it had seized more than 15 million counterfeit products the previous year, blocking most of the bogus listings before they ever went live (ET Retail). Behind that success story is an uncomfortable truth for the rest of the market: counterfeiters have shifted from shipping boxes of knock-off goods to uploading flawless, AI-generated product photos that look just as convincing as the real thing. The race to protect brand trust has moved from warehouses to pixels.

This article explains why fake product listing images have become so difficult to spot, which detection tactics still work in 2025 and how marketplaces and brand owners can build a layered product authenticity check that keeps synthetic media fraud out of the customer journey.

The counterfeit pixel pipeline is growing

Generative AI has democratized high-quality product photography. Anyone with a laptop can produce studio-grade pictures in minutes, complete with perfect lighting, realistic textures and shadow-free backgrounds. A fraudster no longer needs to hold inventory or even own the item that appears in the photo. By combining text-to-image prompts with logo cloning utilities, they can create deepfake product images of the latest sneaker drop or luxury watch, then feed those assets to listing bots that swarm multiple marketplaces overnight.

Three trends amplify the threat:

• Marketplace growth outpaces human moderation. Global e-commerce listings top multiple billions, yet trust and safety teams have not grown at the same rate. Manual reviewers can inspect only a small fraction of new uploads.
• Supply-chain images are already compressed. Brands often provide compressed or resized catalog pictures to resellers, which erases much of the metadata that could help auditors verify provenance.
• Toolchains have evolved. Modern text-to-image systems create visuals from scratch, avoiding the Photoshop artifacts and copy-paste edges that older fraud filters were trained to catch.

The net result is a spike in synthetic media fraud in e-commerce. Shoppers receive counterfeit goods or sometimes nothing at all, brands suffer chargebacks and marketplaces risk regulatory fines for leaving consumers unprotected.

Why first-line defenses fall short

Classic content review still starts with two questions: Does the picture match the listing text, and does it break any obvious policy rules? That surface inspection can catch mismatches such as a phone photo attached to a laptop description, but it rarely uncovers advanced manipulation. Modern converters strip EXIF data entirely, so missing camera tags no longer raise a red flag. Watermark removal algorithms erase partial brand marks. Upscaling models add new pixel information that hides previous compression footprints.

Even automated systems that lean on perceptual hashing can struggle. Perceptual hashes are robust against slight color changes, yet they fail when a fraudster prompts a text-to-image tool to output an image from scratch. The new file shares no overlapping areas with an original, so the hash comparison returns a false negative.

These gaps allow several attack vectors to flourish:

1. AI-generated product photos marketplace detection loophole. Freshly generated images appear unique to the hashing backend, bypassing duplicate checks.
2. Manipulated catalog photos in e-commerce. Fraudsters alter background color, crop away telltale defects or replace the brand logo with an updated version.
3. Reskinned counterfeits among legitimate seller images. A single authentic-looking hero shot is enough to persuade the buyer, even if the item shipped later is an imitation.

Building a layered authenticity check that works in 2025

A single technique rarely covers the full attack surface. Effective protection relies on multiple complementary tests, each examining a different fingerprint of authenticity.

Pixel-level forensic analysis Discriminative models trained on GAN fingerprints can identify subtle irregularities such as over-smooth textures or repetition artifacts that humans miss. A computed heatmap highlights high-risk areas and provides reviewers with visual context instead of a binary pass or fail.

Advanced metadata and C2PA extraction Where available, C2PA signatures bind an image to its capture device and editing history. Fraudsters know this and often delete or corrupt the manifest. Automated checks should validate the cryptographic chain and flag missing or manipulated entries. For a deeper dive on what C2PA can and cannot guarantee, see Vaarhaft’s post C2PA under the microscope.

Cross-platform reverse image search Fraudsters re-use the same hero photo across multiple seller accounts. By computing a privacy-preserving fingerprint of each image and searching the open web, a platform can uncover near duplicates and link suspicious sellers.

Safe, live recapture for high-value listings When an upstream check marks an image as suspicious, marketplaces can ask the seller to retake the photos using a secure web camera session. Vaarhaft’s SafeCam verifies that the picture is captured in real time, detects screen re-photography attempts and immediately runs the new shots through the same forensic pipeline.

Fraud Scanner for automated scalability Vaarhaft’s Fraud Scanner API orchestrates these steps in sequence. The modular service applies AI generation and manipulation detection, manipulation heatmaps, metadata audits, internet reverse search and duplicate discovery on every upload, then returns a single risk verdict helping to navigate further decision making.

Implementation roadmap for marketplace and brand teams

Below is a practical sequence to roll out a robust fake listing photo detection program.

• Prioritize high-risk categories. Luxury fashion, electronics and supplements attract counterfeiters first because margins are highest. Set lower risk thresholds here.
• Activate multi-layer image analysis on seller onboarding. Require the first batch of catalog photos to pass pixel forensics and metadata checks before the account can publish listings.
• Schedule continuous rescans. Fraudsters may swap pictures after passing the initial review. Run nightly jobs that index existing images and compare fingerprints for sudden changes.
• Share intelligence across business units. If you operate both a marketplace and first-party retail channel, synchronize duplicate detection to block bad actors at the border.
• Track precision and recall. Collect reviewer feedback to fine-tune threshold settings and minimize false positives, preserving seller experience.

Secure the customer journey and stay ahead of synthetic media fraud

Deepfake product images have transformed the economics of counterfeiting. A lone keyboard artist can generate thousands of convincing visuals overnight, erasing the barrier to entry once imposed by photography costs. Yet the defense playbook has evolved as well. By combining pixel forensics, structured provenance data and trusted recapture, marketplaces and brands can filter out fake product images before they erode consumer trust.

For additional perspective on how digital fraud trends affect merchant operations, you may also find our earlier analysis of return-abuse patterns useful.

If you would like to explore how Fraud Scanner and SafeCam can strengthen visual brand protection in your own catalog workflow, the Vaarhaft team will be happy to walk you through a short demo.