|
|
|
|
|
|
Study on Rapid Spectral Reappearing and Hyperspectral Classification of Invisible Writing |
LI Yun-peng, DAI Xue-jing, WANG Meng, WANG Dan, GAO Yi, WANG Ming-jiu, SHI Xin-lin, LI Ming-ze |
|
|
Abstract Rapid and non-destructive detection of invisible handwriting, such as erasure, steganography and covering, is a research difficulty in the field of forensic scientific document inspection. In the current research, the method of switching multi-band light source and filter is mostly used to visualise the invisible handwriting, but the spectroscopy mechanism of invisible handwriting is less analyzed. Therefore, the efficiency of invisible handwriting and the success rate of testing are both not high. In order to improve the efficiency and accuracy of erasure, steganography and covering in document examination, the mechanism and rapid visualization method of the three kinds of invisible handwriting were studied by measuring the excitation and fluorescence spectra, reflection and transmission spectra, and micro-topography. Based on the hyperspectral imaging technology of liquid crystal tunable filter (LCTF) and support vector machine (SVM) classification algorithm, a rapid test method for simultaneous display and classification of invisible handwriting is proposed. Chenguang and Pilot erasable pen, fluorescent writing pen and lemon juice emit strong fluorescence under the excitation of 365 nm long-wave ultraviolet light. The fluorescence wavelength of erasable pen and lemon juice is about 716 nm, and the fluorescence wavelength of fluorescent writing pen is 447 nm. Besides the lemon juice invisible writing can also be effectively visualized by using 254 or 365 nm ultraviolet reflection imaging. In the study of covering handwriting, it is found that the transmittance of a ballpoint pen, marker pen and erasable pen is more than 60% in the infrared band of 700~2 500 nm, and the transmittance of a gel pen is less than 20%. Therefore, the near-infrared band of 850 nm imaging is used to effectively visualize the Chenguang gel pen covered by a Pilot ballpoint pen. The LCTF hyperspectral camera was used to image the three kinds of invisible handwriting in the range of 400~720 nm with a step of 5 nm, and the different handwriting in the image were visualized classified simultaneously by SVM classification algorithm, the total classification accuracy was 99.284 4%, and the Kappa coefficient was 0.959 1. Photoluminescence imaging using a 365 nm light source as an excitation light can effectively visualize erasure and steganography handwriting. Because the reflectivity of different inks in the near-infrared band is quite different, near-infrared imaging can effectively visualize the covering handwriting. SVM classification technology based on LCTF hyperspectral imaging can realize the simultaneous display and classification of different types of invisible handwriting and has high visualization efficiency and classification accuracy.
|
Received: 2021-07-02
Accepted: 2021-09-18
|
|
|
[1] Deviterne-Lapeyre C M. Forensic Science International: Synergy, 2020, 2: 429.
[2] Asicioglu F, Tekin T, Ozbek N, et al. Journal of Forensic Sciences, 2019, 64(6): 1898.
[3] Anamika D, Kumar S. Journal of University of Shanghai for Science and Technology, 2020, 22(10): 500.
[4] ZHAO Xue-jun, HUANG Xiao-chun, WANG Chang-liang, et al(赵雪珺,黄晓春,王长亮,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40(3): 674.
[5] Chayal V M, Handa D R, Singh J, et al. Australian Journal of Forensic Sciences, 2015, 48(5): 601.
[6] Gazy M B, Eldebss T, El-Zawawy W K, et al. Nature and Science, 2015, 13(12): 16.
[7] Eldebss T M A, El-Zawawy W K, Gazy M B, et al. Journal of American Science, 2015, 11(11): 30.
[8] Lee J, Kong S G, Kang T Y, et al. Forensic Science International, 2014, 236: 77.
[9] Upadhyay S, F Fatima. Research Journal of Pharmacy & Technology, 2017, 10(12): 4209.
[10] YANG Yu-zhu (杨玉柱). Journal of Criminal Investigation Police University of China(中国刑警学院学报), 2019, 151: 86.
[11] Pandey R K, Sankhla M S, Kumar R. Galore International Journal of Applied Sciences & Humanities. 2018; 2(1): 46.
[12] HOU Jin-ling, ZHANG Jian(侯进令, 张 剑). Forensic Science and Technology(刑事技术), 2015, 40(2): 135.
[13] ZHANG Ling-yan, WANG Lu-xin(张凌燕, 王璐鑫). Journal of People’s Public Security University of China·Science and Technology(中国人民公安大学学报·自然科学版), 2020, 26(3): 1.
[14] Nora M. Hilal, Rania H Twfiq. Egyptian Journal of Chemistry,2020, 63(2), 653.
[15] LIAN Yuan-yuan, LI Wei, WANG Gui-qiang, et al(连园园, 李 伟, 王桂强, 等). Forensic Science and Technology(刑事技术), 2011, 1: 23.
[16] SUI Chun-lai, TAN Yong, ZHANG Ye, et al(隋春来, 谭 勇, 张 烨,等). Acta Optica Sinica(光学学报), 2021, 1-16(网络首发). http://kns.cnki.net/kcms/detail/31.1252.o4.20210204.0948.003.html.
[17] XU Jing-yang, FANG Shao-bo, ZHOU Jing(徐静阳, 方少波, 周 婧). Acta Physica Sinica(物理学报), 2019, 68(6): 7.
[18] SUN Mei, CHEN Xing-hai(孙 梅, 陈兴海). Science Technology and Engineering(科学技术与工程), 2015, 15(22): 167. |
[1] |
LEI Hong-jun1, YANG Guang1, PAN Hong-wei1*, WANG Yi-fei1, YI Jun2, WANG Ke-ke2, WANG Guo-hao2, TONG Wen-bin1, SHI Li-li1. Influence of Hydrochemical Ions on Three-Dimensional Fluorescence
Spectrum of Dissolved Organic Matter in the Water Environment
and the Proposed Classification Pretreatment Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 134-140. |
[2] |
GU Yi-lu1, 2,PEI Jing-cheng1, 2*,ZHANG Yu-hui1, 2,YIN Xi-yan1, 2,YU Min-da1, 2, LAI Xiao-jing1, 2. Gemological and Spectral Characterization of Yellowish Green Apatite From Mexico[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 181-187. |
[3] |
HAN Xue1, 2, LIU Hai1, 2, LIU Jia-wei3, WU Ming-kai1, 2*. Rapid Identification of Inorganic Elements in Understory Soils in
Different Regions of Guizhou Province by X-Ray
Fluorescence Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 225-229. |
[4] |
WANG Hong-jian1, YU Hai-ye1, GAO Shan-yun1, LI Jin-quan1, LIU Guo-hong1, YU Yue1, LI Xiao-kai1, ZHANG Lei1, ZHANG Xin1, LU Ri-feng2, SUI Yuan-yuan1*. A Model for Predicting Early Spot Disease of Maize Based on Fluorescence Spectral Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3710-3718. |
[5] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
[6] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
[7] |
SONG Yi-ming1, 2, SHEN Jian1, 2, LIU Chuan-yang1, 2, XIONG Qiu-ran1, 2, CHENG Cheng1, 2, CHAI Yi-di2, WANG Shi-feng2,WU Jing1, 2*. Fluorescence Quantum Yield and Fluorescence Lifetime of Indole, 3-Methylindole and L-Tryptophan[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3758-3762. |
[8] |
YANG Ke-li1, 2, PENG Jiao-yu1, 2, DONG Ya-ping1, 2*, LIU Xin1, 2, LI Wu1, 3, LIU Hai-ning1, 3. Spectroscopic Characterization of Dissolved Organic Matter Isolated From Solar Pond[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3775-3780. |
[9] |
YUAN Wei-dong1, 2, JU Hao2, JIANG Hong-zhe1, 2, LI Xing-peng2, ZHOU Hong-ping1, 2*, SUN Meng-meng1, 2. Classification of Different Maturity Stages of Camellia Oleifera Fruit
Using Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3419-3426. |
[10] |
SHEN Ying, WU Pan, HUANG Feng*, GUO Cui-xia. Identification of Species and Concentration Measurement of Microalgae Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3629-3636. |
[11] |
XIE Peng, WANG Zheng-hai*, XIAO Bei, CAO Hai-ling, HUANG Yi, SU Wen-lin. Hyperspectral Quantitative Inversion of Soil Selenium Content Based on sCARS-PSO-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3599-3606. |
[12] |
YANG Lei1, 2, 3, ZHOU Jin-song1, 2, 3, JING Juan-juan1, 2, 3, NIE Bo-yang1, 3*. Non-Uniformity Correction Method for Splicing Hyperspectral Imager Based on Overlapping Field of View[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3582-3590. |
[13] |
LI Xiao-li1, WANG Yi-min2*, DENG Sai-wen2, WANG Yi-ya2, LI Song2, BAI Jin-feng1. Application of X-Ray Fluorescence Spectrometry in Geological and
Mineral Analysis for 60 Years[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 2989-2998. |
[14] |
DONG Jian-jiang1, TIAN Ye1, ZHANG Jian-xing2, LUAN Zhen-dong2*, DU Zeng-feng2*. Research on the Classification Method of Benthic Fauna Based on
Hyperspectral Data and Random Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3015-3022. |
[15] |
XUE Fang-jia, YU Jie*, YIN Hang, XIA Qi-yu, SHI Jie-gen, HOU Di-bo, HUANG Ping-jie, ZHANG Guang-xin. A Time Series Double Threshold Method for Pollution Events Detection in Drinking Water Using Three-Dimensional Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3081-3088. |
|
|
|
|