Quick response codes are widely used as anti-counterfeiting labels in the field of product packaging, but they are easily illegally copied. Thus, this paper introduces a quick response code verification method that combines an anti-counterfeiting pattern with a deep feature fusion network. Firstly, a specialized anti-counterfeiting quick response code is designed, composed of a standard quick response code and an anti-counterfeiting pattern, which is essentially a fine-grained random texture distribution sensitive to copying. Next, the anti-counterfeiting patterns are overlapped and divided into blocks during the data processing, which effectively expands the data volume and avoids the interference of pattern content on the authenticity identification. Then, a convolutional self-learning preprocessing layer is employed to initially learn the feature information that represents the difference between authenticity and forgery. Finally, a multi-feature fusion convolutional neural network is proposed to identity the authenticity of anti-counterfeiting patterns. The proposed network comprises two branches, facilitating multi-scale feature extraction and fusion. The effectiveness of the proposed approach is evaluated on a self-constructed quick response code dataset, and the experimental results demonstrate that the proposed approach outperforms traditional knowledge engineering methods and similar deep learning methods.

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.

To meet the critical strategic demands of deep-sea and polar exploration, there is an urgent need to develop a new generation of marine exploration equipment with enhanced corrosion resistance, drag reduction, and ice prevention capabilities. High-entropy alloy (HEA) coatings are an effective way to address these problems, offering excellent corrosion resistance, high mechanical properties, and drag-reduction characteristics. Laser-directed energy deposition (LDED) additive manufacturing is an advanced manufacturing technique for fabricating HEA coatings, enabling the deposition of components on complex curved surfaces while delivering excellent overall performance. However, the inherent cracking problem in LDED-processed HEAs accelerates rapid damage and coating failure. In this work, the FeCoCrNiAl HEA coatings were fabricated on the GCr18Mo substrate using LDED. The crack types and underlying cracking mechanisms, as well as crack-induced hardness degradation mechanisms, are elucidated. The results show that the defect types include solidification cracks, heat-affected zone (HAZ) cracks, and gas pores, with solidification cracks accounting for a substantial proportion. The hot stress in the molten pool exceeds the coating strength. The residual tensile stress (~ 900 MPa) in the HAZ exceeds the yield strength of the matrix, while stress fluctuations in the molten pool during solidification triggered crack initiation. The cracking mechanism is closely related to grain orientation, dislocation accumulation, and carbide. The geometrically necessary dislocation density at the crack (~ 1.33 × 1014 m−2) is higher than that in the matrix. Hard carbide particles play a critical role in crack initiation. Moreover, solidification cracks are a mixture of trans-granular and inter-granular cracks. The hardness of the molten pool, HAZ, and interface zones is higher than that of the substrate, with the HAZ exhibiting a hardness 3.53 times higher. Cracks cause a localized reduction in hardness, with the most pronounced decrease occurring in the solidification crack region. Clarifying the cracking mechanism of the LDED-manufactured HEA coatings will contribute to breakthroughs in marine engineering equipment.