Work

Physics-Constrained Deep Learning for Security Ink Colorimetry with Attention-Based Spectral Sensing

Year: 2024

Type: article

Abstract: The proliferation of sophisticated counterfeiting poses critical challenges to global security and commerce, with annual losses exceeding $2.2 trillion. This paper presents a novel physics-constrained deep learning framework for high-precision security ink colorimetry, integrating three key innovations: a physics-informed neural architecture achieving unprecedented color prediction accuracy (CIEDE2000 (ΔE00): 0.70 ± 0.08, p < 0.001), advanced attention mechanisms improving feature extraction efficiency by 58.3%, and a Bayesian optimization framework ensuring robust parameter tuning. Validated across 1500 industrial samples under varying conditions (±2 °C, 30–80% RH), this system demonstrates substantial improvements in production efficiency with a 50% reduction in rejections, a 35% decrease in calibration time, and 96.7% color gamut coverage. These achievements establish new benchmarks for security printing applications and provide scalable solutions for next-generation anti-counterfeiting technologies, offering a promising outlook for the future.

Source: Sensors

DOI: 10.3390/s25010128

Authors: Po-Tong Wang, Chiu Wang Tseng, Li-Der Fang

Institutions: Lunghwa University of Science and Technology, National Taiwan University

Language: English

Topic: Color Science and Applications

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,955

Funder: Ministry of Education, India