The market is saturated with fake products. StegVision developed an anti-counterfeit technology that combines artificial intelligence, machine learning, computer vision, and steganography to address this issue. The technology is able to scan specially encoded tags and labels for secure product authentication.
Currently, StegVision is in beta. It will continue to develop as we work to understand the needs of brand authenticity and consumer engagement.
Billions of dollars are lost every year because of fake apparel. Not only are brand images and profit revenues hurt when inauthentic products hit the public, consumers risk getting scammed with subpar goods too. StegVision helps consumers in verifying the authenticity of products and allows for brands to create a relationship with them.
It was important to target the fradulent market at a manufacturing level—before fakes have a chance to reach consumers. The technology creates unique, encoded tags. Using a mobile app, consumers can scan tags to confirm a product’s authenticity as well as learn more about the brand and product. This interaction gives brands and consumers a chance for direct connection and a future relationship.
The onboarding and authentication processes are streamlined to efficiently give users the product information and support they need.
The camera viewport was the most important screen within the app because the technology needed a well taken photo to authenticate a label. It underwent multiple iterations to refine the hierarchy of the camera elements, such as flash, manual mode, and help.
To teach users how to send in product photos, StegVision implements a series of in-app steps. This instructs the users in real-time how to leverage the app for the most optimized authentication experience.
Consumers can verify the authenticity of the product in their hands within seconds.
Even if a consumer's scan fails to authenticate a product, StegVision can detect part of the product's label. The app then guides consumers in capturing a more accurate photo through overlays to verify the product's encoding for authentication.