Traceability via quick response (QR) codes is regarded as a clever way to learn specifics about a product’s history, from its creation to its transit and preservation before reaching consumers. The QR code can, however, be easily copied and faked. Therefore, we suggest a novel strategy to prevent tampering with this code. The method is divided into two primary phases: concealing a security element in the QR code and determining how similar the QR code on the goods is to the real ones. For the first problem, error-correcting coding is used to encode and decode the secret feature in order to manage faults in noisy communication channels. A deep neural network is used to both conceal and extract the information encoded in a QR code, and the suggested network creates watermarked QR code images with good quality and noise tolerance. The network has the ability to be resilient to actual distortions brought on by the printing and photographing processes. In order to measure the similarity of QR codes, we create neural networks based on the Siamese network design. To assess whether a QR code is real or fraudulent, the hidden characteristic extracted from the acquired QR code and the outcome of QR code similarity estimation are merged. With an average accuracy of 98%, the proposed technique performs competitively and has been used in practice for QR code authentication.