Counterfeit products have always been more than a nuisance. They undercut legitimate businesses, mislead consumers, and erode trust in entire marketplaces. In 2024, Amazon identified, seized, and destroyed more than 15 million counterfeit items worldwide, preventing them from ever reaching customers or re-entering the supply chain. This isn’t just about knock-off handbags or imitation sneakers, some of these products were in categories like electronics, baby care, and health, where a fake can pose serious safety risks.
The sheer scale of Amazon’s operation makes the challenge especially complex. Millions of new listings are created every day across dozens of countries, involving countless sellers, product types, and languages. Traditional methods for dealing with counterfeits, such as manual reviews, brand-owner reports, or customer complaints, simply cannot keep pace. These approaches rely on spotting a fake after it has been listed, or worse, after a purchase has already been made. Meanwhile, counterfeiters move quickly, exploiting every gap they can find, from timing their listings to avoid scrutiny, to manipulating reviews to appear legitimate.
But counterfeit listings are only one side of the problem. Return fraud has become an equally serious and still largely unsolved issue. Scammers often purchase genuine products, replace them with counterfeits or damaged goods, and send them back for refunds, leaving both Amazon and legitimate sellers absorbing the loss. The combination of fake listings and fraudulent returns creates a loop that’s extraordinarily difficult to detect at scale, blurring the line between buyer, seller, and fraudster.
From Reaction to Prevention: How to Detect Fake Products
Amazon’s strategy has shifted in recent years from reacting to fakes to preventing them from appearing in the first place. The company has invested heavily in artificial intelligence to identify and block suspicious listings before they ever go live. According to its 2024 Brand Protection Report, these AI-driven systems blocked over 99% of suspected infringing listings before a brand even had to report them. That’s a dramatic change from the earlier model, where detection often happened only after a customer flagged the issue.
The technology behind this is both broad and deep. Every product listing is scanned in real time using machine learning models that look for anomalies in seller behavior, unusual pricing patterns, or inconsistencies in product images. Amazon uses computer vision to compare uploaded photos with authentic brand imagery, spotting discrepancies in logos, packaging, and product details. Natural language processing analyzes the text of listings to catch misleading claims or non-standard descriptions often associated with counterfeit goods. On top of that, behavioral analytics track seller activity over time, flagging sudden spikes in listings, unusual changes in shipping practices, or suspicious review patterns.
Each time the system removes a fake listing or bans a fraudulent seller, it learns from that example. Then it refines its models for the next detection. This continuous learning loop is essential because counterfeiters adapt quickly, changing keywords, swapping images, or creating new seller accounts to evade detection.
Progress You Can Measure
The results are tangible. Alongside the 15 million counterfeit items seized and destroyed last year, Amazon blocked thousands of fake seller accounts before they made a single sale. The number of valid infringement notices submitted by brands dropped by 35%, a sign that fakes are being caught earlier in the process. And through its Transparency program, a product serialization system that allows customers to verify authenticity, Amazon has confirmed the genuineness of over 2.5 billion product units. That program now includes 88,000 brands worldwide, from Fortune 500 companies to startups and small businesses.
This level of enforcement is no longer unique to Amazon. Other major e-commerce platforms, from eBay to Alibaba, are also deploying AI to fight counterfeits. Governments in multiple markets are pushing for stronger consumer protections. Artificial intelligence is becoming a standard compliance tool for marketplaces that want to avoid regulatory penalties and reputational damage.
The difficulty lies in the nature of the problem. Counterfeit listings evolve constantly. Sellers alter their tactics as soon as detection methods improve, meaning AI systems must adapt just as quickly. The diversity of languages, product categories, and regional marketplaces adds layers of complexity. Some counterfeits appear in obscure categories where there is little reference data, while others vanish within hours, giving detection systems a narrow window to act.
Amazon’s AI doesn’t work in isolation. Thousands of employees, including machine learning scientists, software developers, investigators, and legal experts, form the human layer that supports the technology. They handle cases the AI can’t easily decide, such as appeals from legitimate sellers wrongly flagged by the system. Brand enforcement teams work directly with rights holders, while legal teams pursue litigation against bad actors. Since launching its Counterfeit Crimes Unit in 2020, Amazon has taken action against more than 24,000 bad actors through lawsuits and criminal referrals.
What It Means for Shoppers and Sellers
For customers, the benefits are straightforward. There is now a lower risk of receiving counterfeit goods, particularly in high-sensitivity categories like health and safety products. For legitimate sellers, stronger protections mean less competition from fake listings that copy their products or undercut their prices. However, sellers also face the possibility of false positives, being flagged for suspicious activity when none exists. Amazon says it is working to improve transparency in these cases and make its appeals process faster and more predictable.
The real win for Amazon is not just removing fake products, it’s protecting trust in its marketplace. Online shopping depends on the assumption that what you order is what you’ll receive. Every counterfeit listing blocked is one less opportunity for that trust to be damaged. The challenge is not to reach perfection, which is impossible. It is to maintain consistency, scalability, and continuous improvement in the fight against counterfeits.
This is not a flashy initiative. Amazon hasn’t built a marketing campaign around its AI counterfeiting tools. The work happens quietly, embedded in the daily operation of one of the world’s largest marketplaces. The tools improve in the background, the counterfeiters adapt, and the systems adapt in return. In an environment where threats evolve daily, the fact that these protections are becoming routine. It is perhaps the clearest sign of progress.
Counterfeiters will continue to look for new ways to slip through. But with AI baked into the marketplace’s infrastructure, the balance of advantage is shifting. For customers, that means more confidence in the “Buy Now” button. For sellers, it means a fairer playing field. And for Amazon, it means that trust, its most valuable asset, remains intact.
