This paper designs a texture-hidden QR code to prevent the illegal copying of a QR code due to its lack of anti-counterfeiting ability. Combining random texture patterns and a refined QR code, the code is not only capable of regular coding but also has a strong anti-copying capability. Based on the proposed code, a quality assessment algorithm (MAF) and a dual feature detection algorithm (DFDA) are also proposed. The MAF is compared with several current algorithms without reference and achieves a 95% and 96% accuracy for blur type and blur degree, respectively. The DFDA is compared with various texture and corner methods and achieves an accuracy, precision, and recall of up to 100%, and also performs well on attacked datasets with reduction and cut. Experiments on self-built datasets show that the code designed in this paper has excellent feasibility and anti-counterfeiting performance.

The widespread presence of security patterns in modern anti-forgery systems has given rise to an urgent need for reliable smartphone authentication. However, persistent recognition inaccuracies occur because of the inherent degradation of patterns during smartphone capture. These acquisition-related artifacts are manifested as both spectral distortions in high-frequency components and structural corruption in the spatial domain, which essentially limit current verification systems. This paper addresses these two challenges through four key innovative aspects: (1) It introduces a chromatic-adaptive coupled oscillation mechanism to reduce noise. (2) It develops a DFT-domain processing pipeline. This pipeline includes micro-feature degradation modeling to detect high-frequency pattern elements and directional energy concentration for characterizing motion blur. (3) It utilizes complementary spatial-domain constraints. These involve brightness variation for local consistency and edge gradients for local sharpness, which are jointly optimized by combining maximum a posteriori estimation and maximum likelihood estimation. (4) It proposes an adaptive graph-based partitioning strategy. This strategy enables spatially variant kernel estimation, while maintaining computational efficiency. Experimental results showed that our method achieved excellent performance in terms of deblurring effectiveness, runtime, and recognition accuracy. This achievement enables near real-time processing on smartphones, without sacrificing restoration quality, even under difficult blurring conditions.

This paper proposes an innovative color anti-counterfeiting quick response code system with a robust authentication mechanism, specifically designed to overcome the limitations of conventional digital anti-counterfeiting technologies. The proposed solution offers four distinct advantages: low implementation cost, superior resistance to forgery, seamless compatibility with standard smartphone cameras, and reliable performance under diverse interference conditions. The technical foundation of our approach combines color multi-channel modulation with advanced halftone theory to generate intricate visual patterns that significantly improve counterfeit resistance. These patterns exhibit two essential security properties: heightened sensitivity to duplication attempts and exceptional resilience against forgery techniques. The authentication system employs a sophisticated dual-spectral analysis methodology that effectively integrates wavelet-domain and Fourier-domain techniques. The wavelet-domain component analyzes macroscopic structural features through scale-dependent approximation coefficients, enabling rapid counterfeit detection. Simultaneously, the Fourier-domain component examines microscopic texture characteristics via localized time-frequency analysis, providing additional security verification. Experimental results demonstrate the superior anti-counterfeiting performance of our scheme, particularly in scenarios with diverse types of interference. This method offers a practical and convenient solution for applications such as identity verification, product identification, medicine traceability, and food safety.