RINQC: A Robust Invisible Network for Quick Response Code

The widespread application of Quick Response Code urgently necessitates en-hancing their traceability and anti-counterfeiting capabilities. However, traditional QR code-based watermark protection technology is susceptible to interference during cross-media transmission. To address the above mentioned issue, this pa-per proposes a QR image watermark algorithm based on the UNet__ network. First, leveraging the multi-angle and high-speed recognition characteristics of QR codes, targeted improvements are made to the network structure and training process, with dilated convolutions incorporated into the encoder to enhance detail precision. Then, a refined local discrimination is achieved through the integration of PatchGAN, continuously optimizing the watermark embedding method to im-prove the imperceptibility of the watermark. Finally, a distortion network mecha-nism is introduced during the training process to simulate the environment of cap-turing QR codes from different angles, thereby enhancing the robustness of the images. Experiments demonstrate that the proposed method achieved PSNR and SSIM values of 36.27 dB and 0.978 respectively, with better robustness and im-perceptibility.