Physics-Constrained Deep Learning for Security Ink Colorimetry with Attention-Based Spectral Sensing
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 innovati...
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/128 |
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| author | Po-Tong Wang Chiu Wang Tseng Li-Der Fang |
| author_facet | Po-Tong Wang Chiu Wang Tseng Li-Der Fang |
| author_sort | Po-Tong Wang |
| collection | DOAJ |
| description | 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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>Δ</mo><msub><mi>E</mi><mn>00</mn></msub></mrow></semantics></math></inline-formula>): 0.70 ± 0.08, <i>p</i> < 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. |
| format | Article |
| id | doaj-art-7a598051ed334bb0b2b02db568e4939a |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-7a598051ed334bb0b2b02db568e4939a2025-01-10T13:20:58ZengMDPI AGSensors1424-82202024-12-0125112810.3390/s25010128Physics-Constrained Deep Learning for Security Ink Colorimetry with Attention-Based Spectral SensingPo-Tong Wang0Chiu Wang Tseng1Li-Der Fang2Department of Electrical Engineering, Lunghwa University of Science and Technology, Taoyuan 333326, TaiwanDepartment of Biomechatronics Engineering, National Taiwan University, Taipei 10617, TaiwanDepartment of Electrical Engineering, Lunghwa University of Science and Technology, Taoyuan 333326, TaiwanThe 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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>Δ</mo><msub><mi>E</mi><mn>00</mn></msub></mrow></semantics></math></inline-formula>): 0.70 ± 0.08, <i>p</i> < 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.https://www.mdpi.com/1424-8220/25/1/128physics-constrained deep learningattention-based modelingspectral color sensingsecurity ink colorimetryBayesian optimizationanti-counterfeiting systems |
| spellingShingle | Po-Tong Wang Chiu Wang Tseng Li-Der Fang Physics-Constrained Deep Learning for Security Ink Colorimetry with Attention-Based Spectral Sensing Sensors physics-constrained deep learning attention-based modeling spectral color sensing security ink colorimetry Bayesian optimization anti-counterfeiting systems |
| title | Physics-Constrained Deep Learning for Security Ink Colorimetry with Attention-Based Spectral Sensing |
| title_full | Physics-Constrained Deep Learning for Security Ink Colorimetry with Attention-Based Spectral Sensing |
| title_fullStr | Physics-Constrained Deep Learning for Security Ink Colorimetry with Attention-Based Spectral Sensing |
| title_full_unstemmed | Physics-Constrained Deep Learning for Security Ink Colorimetry with Attention-Based Spectral Sensing |
| title_short | Physics-Constrained Deep Learning for Security Ink Colorimetry with Attention-Based Spectral Sensing |
| title_sort | physics constrained deep learning for security ink colorimetry with attention based spectral sensing |
| topic | physics-constrained deep learning attention-based modeling spectral color sensing security ink colorimetry Bayesian optimization anti-counterfeiting systems |
| url | https://www.mdpi.com/1424-8220/25/1/128 |
| work_keys_str_mv | AT potongwang physicsconstraineddeeplearningforsecurityinkcolorimetrywithattentionbasedspectralsensing AT chiuwangtseng physicsconstraineddeeplearningforsecurityinkcolorimetrywithattentionbasedspectralsensing AT liderfang physicsconstraineddeeplearningforsecurityinkcolorimetrywithattentionbasedspectralsensing |