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Co-Founder and Board Director, Ennoventure Inc.
Contrary to popular belief, brand protection goes above and beyond just protecting sales and reputation. It is the set of systems in place to ensure that a brand safeguards its customers, investors, and partners. To secure a brand, you need trust. That trust is built on the foundation of increased interactions and engaging user experiences. Today, brand protection has evolved to include all these factors, apart from the traditional need to protect the intellectual property (IP) of a brand or company. This process makes it such that a third party cannot legally use any IP belonging to the brand without due permission.
The Economic Benefit of Brand Protection
In 2019, the World Economic Forum valued the illicit trade industry at approximately $2.2 trillion. Such a vast market implies that there are economic gains to be made by protecting a brand. When third parties infringe on a brand’s unprotected IP, they take away from the revenue and intangible value what would have originally been attributed to the brand with IP ownership. If the brand is protected adequately, the customer may only purchase authentic products from the official brand. However, if left unprotected, this revenue might go to third parties looking to produce close or identical copies of the same products or designs.
While the most visible and public examples tend to come from the fashion industry, absolutely all manufacturers producing any category of goods must ensure that their brand is well-protected. Today, technology has stepped into such an extent that protection and anti-counterfeiting measures rely on next-gen tech pieces like AI and Machine Learning – the latter of which is of specific interest.
Machine Learning: The Next Step?
Machine Learning (ML) is the process of “learning” through data sets to improve the performance of a process or system. The most frequently encountered example of ML is the Facebook Algorithm, which utilizes user data to provide content most tailored to that particular user. In doing so, ML identifies patterns in the user’s history that allow it to predict what the user would like to consume next.
In brand protection, ML plays a crucial role – it can adapt to changing search patterns on the go. This implies that when counterfeiters try to update their product keywords or try to reproduce the IP or designs of a legitimate brand, ML can identify these changes and flag the same, much faster than a team of people sitting behind computers.
ML, Cryptography and Image Recognition
The agility of ML allows it to work on image recognition features too. As a program, it can determine any image patterns and update its search parameters to find counterfeit material or products in real-time. An ML model can evaluate the image and its quality, and cross-verify the same with any detection of counterfeit goods. In this process, the ML model is able to adapt to changing parameters while also retaining the original data set or image to match the counterfeit ones. Once such a match is made, the ML model can be taught to flag it in the form of actionable data, which is then handed over to the appropriate channels.
This feature is especially useful in terms of product or design identification. ML models can determine the presence of signatures in the artwork on a product or a design. The best part is that it need not hunt for the exact reproduction or replication of the design. Even if there is a slight change in colour, the ML model can be trained to detect such minor variations and flag them. This casts a much wider net than just searching for exact replicas because counterfeiters recognize the ease of being caught whilst duplicating a design. They resort to changes or variations that distort the design just enough to escape detection, while still retaining its major framework.
Armed with cryptography, ML helps in identifying duplicate products efficiently.
The New Leap in Brand Protection
Brands have been able to collect data from items from manufacturing to distribution to retail at lower prices thanks to software algorithms and predictive intelligence – ML in particular. Especially in terms of designs and artwork, ML saves the work of a brand inspector and their team sifting through possible (not even confirmed) cases of counterfeiting or duplication. In a traditional case, the inspector would compare the two designs closely for a long time to determine the validity and the authenticity of one over the other. With the shift to ML, this process is automated to such an extent that the time and resources being poured into the counterfeit detection process are slashed radically.
While brand protection measures sometimes struggle to keep up with the pace of threats in counterfeiting, the advent of AI and Cryptography has enabled brands to detect, track and analyze infringement on their IP. In the future, this leap will become the standard operating procedure for any security measures against possible threats.
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Views expressed above are the author’s own.
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