Future of fashion: technological tools to combat counterfeiting

The fashion industry, a global hub of creativity and commerce, faces a mounting threat from counterfeiting. Fake goods not only erode brand value but also cause massive financial losses and diminish consumer trust. According to an OECD report in 2024, counterfeit products account for up to 2.5% of global trade and are worth around $464 billion, rivalling the GDPs of nations such as Austria and Belgium. Luxury brands are a primary target. The reputed Indian business daily The Economic Times reported in 2024 that counterfeit fashion items make up around 60% of all seized counterfeit goods globally.

The problem has been amplified with the rise of e-commerce. With nearly 30% of all personal luxury sales expected to take place online in 2025, and counterfeit products proliferating across digital marketplaces, a MarkMonitor study has found that nearly one in two brands are losing sales to counterfeits, with one in three reporting revenue drops of 10% or more. Alarmingly, 58% expect the situation to worsen as inflation drives consumers towards cheaper fakes. The emergence of high-quality ‘super fakes’, often produced using the same materials and factories as genuine goods, has further complicated detection.

Consumer trust is faltering as a result. Nearly half of surveyed shoppers worry they may be unknowingly purchasing counterfeits. According to Incopro, an online intellectual property (IP) and brand protection software provider, 66% of those who did so unintentionally said they would never buy from the brand again. Others stop purchasing or warn peers – damaging reputations long-term.

To counter this, brands are turning to AI.

Blockchain technology has emerged as a powerful tool to combat counterfeiting in the fashion industry. By assigning a unique digital fingerprint to luxury products, blockchain securely records an item’s entire life cycle on an immutable ledger. This ensures instant verification of product authenticity for brands and consumers. Prominent fashion houses such as Prada and Louis Vuitton have adopted blockchain, notably through the Aura Blockchain Consortium – co-developed by Prada, LVMH, Cartier, Richemont, and Mercedes-Benz – to enhance product authentication and supply chain transparency. Nike’s patented blockchain-based system, CryptoKicks, assigns a unique digital ID to each pair of shoes for authenticity and ownership verification. Similarly, luxury watch brands employ blockchain technology through platforms such as Origyn to issue digital certificates ensuring product genuineness.

Meanwhile, patents utilising AI such as machine learning, image recognition, and data analytics are instrumental in developing technologies that detect counterfeit goods, authenticate products, and protect brand integrity. In 2024, brands such as Apple, Google, Levi’s, Nike, Ralph Lauren, and Columbia Sportswear adopted patented AI-driven technologies, significantly enhancing product functionality, product personalisation, and sustainability, as well as making items more resistant to counterfeiting.

Europe’s Digital Product Passport (DPP), which is expected to become mandatory between 2026 and 2030, is a significant advancement. Designed to share comprehensive product data across the value chain, the DPP promotes transparency and sustainability. Luxury brands such as Louis Vuitton, Dior, and Prada are utilising these digital passports, supported by platforms such as Arianee, to boost customer engagement and combat counterfeits effectively. Digital product passports typically utilise unique identifiers such as QR codes or RFID (radio-frequency identification) tags to capture product data securely stored on cloud-based platforms or blockchain networks. Access is available via mobile apps or web portals, ensuring reliable and auditable data management. Various technological standards are relied upon regarding the communication and capturing of data, as well as the auditing of the data.

In recent news, China released content on social platforms to showcase the manufacturing of items very similar to luxury goods but being sold at substantially cheaper rates in response to the increased tariffs imposed by the US. This is yet another instance that highlights the necessity of adopting advanced technological means to reassure consumers and ensure no harm comes to brand repute.

When Lacoste became a victim of a sophisticated counterfeiting scheme – wherein scammers purchased genuine products, replaced them with replicas, and returned the fakes for refunds – it partnered with the French tech firm Cypheme in October 2024 to introduce an AI-powered anti-counterfeiting system called Vrai AI across its warehouses. Vrai AI leverages advanced visual analysis techniques, achieving up to 99.7% accuracy in counterfeit detection by comparing suspect items with authenticated product images. Additionally, it employs a specialised “Noise Print AI Label”, a unique digital sticker directly printed on to genuine products, making replication by counterfeiters nearly impossible.

AI-powered image recognition systems are revolutionising counterfeit detection by scanning vast data sets across online marketplaces and social media platforms. These systems identify counterfeit items by comparing uploaded product images with official brand designs. Utilising sophisticated algorithms, AI tools detect subtle differences imperceptible to human eyes, enabling brands to quickly identify and remove fraudulent listings.

For example, luxury resellers and brands increasingly rely on AI solutions such as Entrupy, which boasts an impressive 99.1% accuracy rate in distinguishing authentic products from counterfeits. Entrupy leverages advanced image recognition technology, analysing multiple high-resolution photographs of a product, captured from various angles using a smartphone equipped with a microscopic lens. These images are cross-referenced against an extensive database of authentic and counterfeit items, ensuring precise counterfeit identification. Entrupy also issues certificates of authenticity, backed by financial guarantees, significantly boosting consumer confidence and security in luxury purchases.

At the heart of these advanced systems are convolutional neural networks (CNNs), which are sophisticated deep-learning models that are particularly effective at visual analysis – ideal for counterfeit detection within the fashion industry. Specialised CNN architectures – such as Faster R-CNN, YOLO, and SSD – not only classify images but also precisely identify and locate critical features such as brand logos, patterns, or specific product details.

Effective CNN training begins with high-quality, accurately labelled image data sets sourced from official brand catalogues, manufacturer archives, and collections of known counterfeit images. Initial image processing includes resizing images to standardised dimensions and normalising pixel values to enhance accuracy. Data augmentation techniques – such as image rotations, flips, and scaling – are utilised to diversify data sets and expose the CNN to various visual transformations, reinforcing its ability to generalise and accurately detect counterfeit items.

A typical CNN comprises several essential layers:

• Input layer – receives pre-processed images;

• Convolutional layers – apply learnable filters that detect distinctive features;

• Activation functions – introduce non-linearity, allowing complex patterns to be learnt;

• Pooling layers – reduce spatial dimensions, simplifying computations and focusing on prominent features;

• Fully connected layers – map extracted visual features into recognisable categories or classes; and

• Output layer – provides the final classification, distinguishing genuine products from counterfeits.

By effectively integrating these layers, CNNs excel at identifying and classifying intricate visual details, substantially improving the precision and efficiency of counterfeit detection in the fashion industry.

Companies such as Entrupy, Aura Blockchain, Cypheme, Vrai AI, MarqVision, and Red Points, as well as online fashion marketplaces such as RealReal and Farfetch, have been involved in developing AI-assisted tools and have been filing patents increasingly over recent years.

Meanwhile, a startup called Osmo AI has developed an anti-counterfeiting tool with sensors that work by reading the unique scent signatures of authentic products. Using AI, these sensors can identify counterfeit items with incredible speed and precision. By training on massive data sets, they detect subtle scent patterns while filtering out background smells, providing a novel way to verify product authenticity.

Another way in which AI technology has become integral to safeguarding IP rights in the fashion industry is through the rise of legal-tech solutions that can effectively monitor and enforce IP rights. Automated platforms now proactively monitor digital channels for trademark infringements, issuing real-time alerts that enable swift legal action against counterfeiters. One such platform is Red Points, a Barcelona-based company that automates counterfeit detection across the web. Trusted by global brands such as Fila, Puma, and Hugo Boss, Red Points helps to safeguard brand reputation and reduce revenue loss. Its advanced software identifies and removes infringing content with precision, offering a fast, scalable, and proactive approach to IP protection in the digital age.

As counterfeiting becomes more sophisticated and pervasive – particularly across online platforms – the fashion industry faces mounting financial and reputational risks. With traditional enforcement mechanisms no longer sufficient, AI has emerged as a powerful ally. From image recognition systems powered by CNNs to legal-tech platforms such as Red Points, AI is enabling brands to detect, deter, and dismantle counterfeit operations more effectively.

However, challenges remain. One of them is the high cost of implementing AI-driven solutions. For many fashion companies – particularly smaller ones – the investment required to integrate advanced AI and blockchain technologies may be prohibitive. This often exacerbates the gap between luxury brands and smaller companies. Even for luxury brands, it is a time- and finance-consuming task, as they must continually invest in new technologies to stay ahead of counterfeiters and cyber threats. Another challenge is that of navigating data protection frameworks within the industry; for example, in ascertaining the true identities of cybersquatters.

Be that as it may, in an era when consumer trust is fragile and digital storefronts seemingly infinite, adopting intelligent IP protection is vital for brand survival. Investing in AI-driven anti-counterfeiting solutions could prove a key ally in this milieu.