Abstract: The present disclosure relates to the field of product authentication and verification and discloses a system (100) and method (200) to authenticate a product. The system (100) comprises a memory (102), a scanning module (104), a product identification module (108), a first re-direction module (106) and a second re-direction module (110). The product identification module (108) detects a design printed on the packaging of products and identifies products based on pre-trained machine learning model and detected design. The scanning module (104) facilitates user to scan visual codes printed on products and extracts UIDs and verification URLs from scanned visual code. The first re-direction module (106) receives extracted UID and verification URL if product is identified and screens extracted URL to identify its authenticity. The second re-direction module (110) re-directs users to a third-party verification platform at the embedded URL for product verification if product is not identified.

Abstract: The present disclosure relates to the field of security systems and discloses a tamper detection device. The device (100) comprises at least one transducer (106,110), a power supply unit (114), a logical gate (112), a processing unit (104), and a tamper tag 102). The transducer (106,110) generates a trigger signal upon detection of a tamper event. The logical gate (112) is operable in an open state or a closed state. The processing unit (104) generates a tamper detection signal for changing the state of the logical gate (112) upon receiving the trigger signal or upon detecting loss of power supply from the power supply unit (114). The change in state of logical gate (112) is detected by a set of sensors (116), of the tamper tag (102), which in turn cause an LED indicator (118), of the tamper tag (102), to indicate the state of the device. An auxiliary capacitor (108) is configured to supply power to the processing unit (104) in the absence of the supply of power from the power supply unit (114).

Abstract: The present disclosure relates to the field of security systems and discloses a tamper detection device. The device (100) comprises at least one transducer (106,110), a power supply unit (114), a logical gate (112), a processing unit (104), and, a tamper tag 102). The transducer (106,110) generates a trigger signal upon detection of a tamper event. The logical gate (112) is operable in an open state or a closed state. The processing unit (104) generates a tamper detection signal for changing the state of the logical gate (112) upon receiving the trigger signal or upon detecting loss of power supply from the power supply unit (114). The change in state of logical gate (112) causes a tamper flag value stored in the tamper tag (102) to change, thereby indicating a tampered status to a reader scanning the tamper tag (102). The device (100) detects a tamper event even if the device (100) is not damaged/broken during tampering.

Abstract: The present disclosure relates to the field of product authentication and counterfeit detection and discloses a system and method to detect counterfeit products. The system (100) comprises a first scanner (102), an application (132) loaded in a user device (114) and a server (110). The first scanner (102) scans images of visual codes printed on products manufactured and packaged on a production line and assigns product details to the visual codes. The application (132) facilitates a user to scan said visual codes and redirects the user to an encoded URL to view the product details. The application (132) captures and transmits a browser ID and location data to the server (110) and allows users to provide inputs to a pre-determined set of questions. The server (110) derives scanning indices from the received browser ID and location data and identifies counterfeit products based on said scanning indices and inputs.

Abstract: The present disclosure envisages the field of authenticate a product. A system (100) to authenticate a product comprises a user device (102), a sensing module (104), a reference capturing module (106), a recognition module (108) and a redirection module 110). The user device (102) is configured to scan a visual of a product. The sensing module (104) is configured to receive and sense presence of a unique code in the visual. The reference capturing module (106) is configured to capture, process and generate an ID part one and an ID part two from the received visual. The recognition module (108) located on a remote server (118) processes the ID part one and ID part two to recognize and fetch a validation system credential. The redirection module (110) is configured receive the ID part one and the validation system credential to access a validation system to authenticate the ID part one.

Abstract: The present disclosure relates to the field of security systems and discloses a tamper detection device. The device (100) comprises at least one transducer (106,110), a power supply unit (114), a logical gate (112), a processing unit (104), and, a tamper tag 102). The transducer (106,110) generates a trigger signal upon detection of a tamper event. The logical gate (112) is operable in an open state or a closed state. The processing unit (104) generates a tamper detection signal for changing the state of the logical gate (112) upon receiving the trigger signal or upon detecting loss of power supply from the power supply unit (114). The change in state of logical gate (112) causes a tamper flag value stored in the tamper tag (102) to change, thereby indicating a tampered status to a reader scanning the tamper tag (102). The device (100) detects a tamper event even if the device (100) is not damaged/broken during tampering.

Abstract: In one aspect, a computerized method for anti-counterfeiting solution using a machine learning (ML) model includes the step of providing a pre-defined set of feature detection rules, a pre-defined set of edge detection rules, a pre-defined threshold percentage, an original seal, an original fingerprint of the original seal, and a pre-trained fingerprint identification model. The pre-trained fingerprint identification model is trained by a specified ML algorithm using one or more digital images of the original seal. With a digital camera of a scanning device, the method scans a seal whose authenticity is to be determined. The seal is used to secure a transportation container. The method uses the pre-defined set of feature detection rules to detect and extract an extracted feature image at a specified position on the seal. The method breaks down the extracted feature image of the seal into a ‘kn’ number of sub-images by forming a ‘k’ rows x ‘n’ columns of a grid of the extracted feature image.