Protection and Hiding Algorithm of QR Code Based on Multichannel Visual Masking Quick Response (QR) Code is extensively used due to its advantages in fast readability as well as large capacity. However, when using QR codes, users may suffer from loss of private information because of peeping and scanning by unauthorized attackers. In this paper, we propose a novel algorithm to hide and protect the QR code based on multichannel visual masking. With our algorithm, the appearance of the QR code is dramatically changed while it maintains the original secret information. Unauthorized users can not extract any information from the protected QR code with the standard QR code reader. For authorized users, we design a truth table based decoder that works with the standard QR code reader. Extensive experiments are performed to evaluate the robustness and effectiveness of our method. The codes of this paper are published at https:github.comJiaheZhangProtection QRCodeMVM.

Robust data retrieval from highsecurity structural colour QR codes via histogram equalization and decorrelation stretching In this work, robust readout of the data (232 English characters) stored in highsecurity structural colour QR codes, was achieved by using multiple image processing techniques, specifically, histogram equalization and decorrelation stretching. The decoded structural colour QR codes are generic diffractive RGBpixelated periodic nanocones selectively activated by laser exposure to obtain the particular design of interest. The samples were imaged according to the criteria determined by the diffraction grating equation for the lighting and viewing angles given the red, green, and blue periodicities of the grating. However, illumination variations all through the samples, crossmodule and crosschannel interference effects result in acquiring images with dissimilar lighting conditions which cannot be directly retrieved by the decoding script and need significant preprocessing. According to the intensity plots, even if the intensity values are very close (above 200) at some typical regions of the images with different lighting conditions, their inconsistencies (below 100) at the pixels of one representative region may lead to the requirement for using different methods for recovering the data from all red, green, and blue channels. In many cases, a successful data readout could be achieved by downscaling the images to 300pixel dimensions (along with bilinear interpolation resampling), histogram equalization (HE), linear spatial lowpass mean filtering, and gamma function, each used either independently or with other complementary processes. The majority of images, however, could be fully decoded using decorrelation stretching (DS) either as a standalone or combinational process for obtaining a more distinctive colour definition.