Counterfeiting in Retail: How AI Can Help In Classifying Fakes

Counterfeits are fake goods that pretend to be genuine, including design and packaging, but are inferior in quality and unregulated. Counterfeit goods are being produced, sold, and purchased by people across the globe. 

In some cases, people are aware that they are considering a fake or counterfeit product. Like when they go to a flea market, they know that the pair of shoes they get might not necessarily be genuine. The fakes or replicas serve as an affordable alternative to successful and expensive products. That’s why people buy them.

But the general level of awareness is relatively low. Most of the time, people end up buying bogus products assuming them to be authentic. Also, they pay prices as high as the original products or with little discounts. 

Whatever the case, people buy counterfeit goods. Thus, it is a horribly good business.

Why? The answer is here. 

Reason For Buying Counterfeit Products

If you’ll look at the original and it’s fake, differentiating between the two would be tough.

Try it yourself by taking a pair of sunglasses from Ray-Ban (original) and Ray-Bon (fake). You’ll fail. 

They have such high details that identifying the real one is quite impossible. That’s the secret sauce behind the invincible business of counterfeit goods.

But should we be concerned? Yes, all the time.

Harms of Counterfeiting

Primarily, products that sell because of the brand name suffer from counterfeiting. And trust me, it’s not restricted to fashion, footwear, accessories, and apparel. 

You can find fakes in highly-critical industries such as pharma, construction, cosmetics, agriculture, and automobile. Also, fake products are found in categories like watches, toys, batteries, masks, sanitizers, alcohol, tobacco, coffee, and much more.

We have to understand that substituting fakes for originals can be really dangerous for consumers. Since fakes are made with low-cost resources and materials, they don’t offer what they imitate or intend to. 

For example, fake lipstick can do more harm to the lips than good. It can lead to allergies or some other condition that we never anticipated. Thus, placing the consumer’s life at risk.

Similarly, a fake chainsaw can lead to failures and accidents at the workplace. Think of the many lives that are at risk because of it. The list is never-ending.

Does this hint at how huge is the world of counterfeits? 

Market Size of Counterfeit Goods

Well, ironically, it is a business empire with a valuation of a whopping $600 billion per year. That indicates counterfeits form roughly 3.3% of global trade. It also hints that most of us have come across them irrespective of our will or knowledge.

But, where is the problem? 

As per an estimate, there has been a 10000% growth of fakes or counterfeits. 

That’s alarming for two reasons. 

Firstly, the risk associated with it. (Fakes do not meet any security norms). Secondly, the significant and potential harm it can do to the brand’s reputation. (Even when the brand wasn’t at fault!) 

It’s like the elephant in the room – identifying the fake products so that you can prevent their sale. However, it is a big challenge if you were to do it with the naked eye.

That’s where Artificial Intelligence drops in. 

AI For Classifying Counterfeits

By employing Artificial Intelligence(AI) algorithms, you can gauge a product’s authenticity. You can identify if it is original or counterfeit, both online and offline. (In this discussion, we are focussed on the online detection of counterfeits.)

The technology combines text processing, image processing, and machine learning to detect counterfeits. 

• • Text Processing: Analyzing text written on products to identify logos and brand names.

• • Image Processing: Observing product images to find features that an original product should or shouldn’t have.

• • Machine Learning: Using a supervised or unsupervised learning approach to predict originality.

In supervised learning, the model observes the test product to find its features. Then, it compares those features with features of products in the dataset. Based on the matched features, it concludes whether a product is genuine or not.

The unsupervised learning model observes the test product to identify its features. Then, it clusters or groups it with other products in the dataset. The similarities or differences form the basis of the segmentation. Thus, the differences help in differentiating fake products from original ones.

Whatever the approach, the technology is useful. It automates and assists in recognizing and classifying fakes across industries and verticals. From footwear and beauty products to medicines and toys, you can get hold of fakes with AI. 

Both Businesses & Customers Can Benefit

If you’re selling online, it can scan and process millions of listings every day. It can list out all the links that have counterfeit goods and report them to the concerned website. 

For a business, that would be an ongoing process. It will ensure that forgers are not harming you and more importantly, your customers. 

But, on the flip side, it is more crucial for customers to recognize fakes and refrain from buying them. And there you go.

The technology also benefits customers buying online. By uploading the product link to the model which runs on a web app, they can recognize fakes. 

Once they do so, they can choose to buy only authentic products that give them value for money. Customers feel satisfied and not frustrated after filtering what they ought to buy.

That empowering, isn’t it? Well, it is AI for good.

If you have something in mind that we can work together on, we are right here. Contact us today.