Brand Protection Is The New Rule

Karan Rai is Chief Technology Officer at Ennoventure Inc.

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Most high-end brands have one common enemy—brand identity theft. They are a part of the $11.36 billion industry, and the market doesn't seem to slow down.

In the world of AI, where creating (fake) products takes only a couple of hours, it is easy for brands to lose business. From product synthesizing to implementing vocal mannerisms, it is easy for fraudsters to steal a brand's product and overall identity. Unfortunately, this is not a one-time default either—fraudsters can easily deploy recurrent Neural Networks to learn the product features, its advertising elements, brand colors and others to create a brand new product and fake that is eerily similar to the original one.

This fake product and overall brand identity can lead to a drop in customer loyalty, recall and, ultimately, revenue loss. Having a counterfeit in the market makes the brand less trustworthy and valuable and can be an imminent threat to the brand perception, which may take years to remedy.

While high-end brands have the monetary bandwidth to be identity gatekeepers and curtail losses, brand identity theft spells disaster for small- to medium-sized businesses looking to make a mark in the consumer-driven industry.

The calculated process of brand theft can be countered with a proactive approach to brand protection.

Ways To Ensure Proactive Brand Protection

AI-image Recognition: AI-driven image recognition is a great way to optimize surveillance of counterfeit products across the digital space. ML models can be trained to find product images similar to original products and detect discrepancies such as incorrect fonts, wrong visual placement and wrong listing, to name a few.

Cryptographic Signature: Brands can strengthen their protection with cryptographic signatures. These can be applied as invisible signatures all over the packaging of the original brands' products. These signatures cannot be replicated and can be authenticated within seconds using a smartphone. An additional advantage of these signatures is that they do not disrupt the production or printing process.

Social Listening: AI applications such as sentiment analysis and NLP for detecting intents and context can monitor conversations around the product and track any adverse comments signaling a potential link to copies or counterfeit manufacturers. Noted fashion brands have taken the help of NLP to track communication related to their original products across social media or online review platforms and get real-time alerts against violations.

How AI Is Contributing To Brand Protection

Brand protection has traditionally comprised labels, holograms and QR codes, which are all easy to copy. Al is leveling up the game, making it harder for counterfeiters and increasing product traceability. With AI models acquiring the capability to be run on edge devices, the technology is going to become as ubiquitous as QR scanners and will power our smartphones with faster, reliable and accurate brand protection tools, leading to a safer environment for customers to buy their products with confidence.

Brand protection should be integral to a brand's strategy from the early stages rather than just an afterthought, as it's harder and more expensive to apply it when the product has already scaled and, therefore, more vulnerable to counterfeiting. Better safe than sorry.

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Liquid Neural Networks Do More With Less

[Ramin Hasani] and colleague [Mathias Lechner] have been working with a new type of Artificial Neural Network called Liquid Neural Networks, and presented some of the exciting results at a recent TEDxMIT.

Liquid neural networks are inspired by biological neurons to implement algorithms that remain adaptable even after training. [Hasani] demonstrates a machine vision system that steers a car to perform lane keeping with the use of a liquid neural network. The system performs quite well using only 19 neurons, which is profoundly fewer than the typically large model intelligence systems we've come to expect. Furthermore, an attention map helps us visualize that the system seems to attend to particular aspects of the visual field quite similar to a human driver's behavior.

[Mathias Lechner] and [Ramin Hasani]The typical scaling law of neural networks suggests that accuracy is improved with larger models, which is to say, more neurons. Liquid neural networks may break this law to show that scale is not the whole story. A smaller model can be computed more efficiently. Also, a compact model can improve accountability since decision activity is more readily located within the network. Surprisingly though, liquid neural network performance can also improve generalization, robustness, and fairness.

A liquid neural network can implement synaptic weights using nonlinear probabilities instead of simple scalar values. The synaptic connections and response times can adapt based on sensory inputs to more flexibly react to perturbations in the natural environment.

We should probably expect to see the operational gap between biological neural networks and artificial neural networks continue to close and blur. We've previously presented on wetware examples of building neural networks with actual neurons and ever advancing brain-computer interfaces.

Artificial Neural Network

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An artificial neural network, often just named a neural network, is a mathematical model inspired by biological neural networks. A neural network consists of an interconnected group of artificial neurons, and it processes information using a connectionist approach to computation. In most cases a neural network is an adaptive system changing its structure during a learning phase. Neural networks are used for modeling complex relationships between inputs and outputs or to find patterns in data.

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