Image Steganography-Based GUI Design to Hide Agricultural Data

Abstract

Throughout the ages, safely preserving and transmitting data that have extraordinary importance for humanity has increased its importance with rapid advances in computer technology. Steganography stores hidden data within the files, which are unnoticed by third parties, so it provides secure transmission of data to the receiver. In this study, a steganography-based GUI design has been carried out, which ensures that the agricultural data is safely stored and communicated to the other party. We used LSB one-bit, two-bit, three-bit substitution and PVD algorithms with GUI for stages of agricultural data hiding and extracting at cover images. We also provided extra security using the embedded key and shifting operations on the hidden data before hiding data the cover image. In short, we confused the hidden data in the cover image so that malicious people can't understand. In experimental studies, performance analysis was evaluated by comparing various criteria as similarity ratio (Structural Similarity Index Measure, SSIM), stego image quality (Peak Signal-to-Noise Ratio, PSNR) and data hiding capacity (Payload).

Keywords

• Image steganography
• LSB substitution
• PVD
• Data hiding
• Agriculture data

References

1. [1] Too, E. C., Yujian, L., Njuki, S., Yingchun, L., “A comparative study of fine-tuning deep learning models for plant disease identification.”, Computers and Electronics in Agriculture, (2018).
2. [2] Solak, S., Altinişik, U., “A new method for classifying nuts using image processing and k‐means++ clustering.”, Journal of Food Process Engineering, 41(7), e12859, (2018).
3. [3] Huang, X. Y., Pan, S. H., Sun, Z. Y., Ye, W. T., Aheto, J. H., “Evaluating quality of tomato during storage using fusion information of computer vision and electronic nose.”, Journal of Food Process Engineering, 41(6), e12832, (2018).
4. [4] Kruse, O. M. O., Prats-Montalbán, J. M., Indahl, U. G., Kvaal, K., Ferrer, A., Futsaether, C. M., “Pixel classification methods for identifying and quantifying leaf surface injury from digital images.”, Computers and electronics in Agriculture, 108:155-165, (2014).
5. [5] Vithu, P., Moses, J. A., “Machine vision system for food grain quality evaluation: A review.”, Trends in Food Science & Technology, 56:13-20, (2016).
6. [6] Kumar, K., Kumar, S., Sankar, V., Sakthivel, T., Karunakaran, G., Tripathi, P. C., “Non-destructive estimation of leaf area of durian (Durio zibethinus)–An artificial neural network approach.”, Scientia horticulturae, 219:319-325, (2017).
7. [7] Aydoğan, T., Bayılmış, C., “A new efficient block matching data hiding method based on scanning order selection in medical images.”, Turkish Journal of Electrical Engineering & Computer Sciences, 25(1):461-473, (2017).
8. [8] Ropodi, A. I., Panagou, E. Z., Nychas, G. J., “Data mining derived from food analyses using non-invasive/non-destructive analytical techniques; determination of food authenticity, quality & safety in tandem with computer science disciplines.”, Trends in Food Science & Technology, 50: 11-25, (2016).

9. [9] Rong, D., Ying, Y., Rao, X., “Embedded vision detection of defective orange by fast adaptive lightness correction algorithm.”, Computers and Electronics in Agriculture, 138:48-59, (2017).
10. [10] Beyaz, A., Özkaya, M. T., İçen, D., “Identification of some spanish olive cultivars using image processing techniques.”, Scientia Horticulturae, 225:286-292, (2017).
11. [11] Peng, Y., Zhang, L., Song, Z., Yan, J., Li, X., Li, Z., “A QR code based tracing method for fresh pork quality in cold chain.”, Journal of Food Process Engineering, e12685, (2018).
12. [12] Kale, A. P., Sonavane, S. P., “IoT based Smart Farming: Feature subset selection for optimized high-dimensional data using improved GA based approach for ELM.”, Computers and Electronics in Agriculture, (2018).
13. [13] Colezea, M., Musat, G., Pop, F., Negru, C., Dumitrascu, A., Mocanu, M., “CLUeFARM: Integrated web-service platform for smart farms.”, Computers and Electronics in Agriculture, 154: 134-154, (2018).
14. [14] Muangprathub, J., Boonnam, N., Kajornkasirat, S., Lekbangpong, N., Wanichsombat, A., Nillaor, P., “IoT and agriculture data analysis for smart farm.”, Computers and Electronics in Agriculture, 156:467-474, (2019).
15. [15] Kamilaris, A., Prenafeta-Boldú, F. X., “Deep learning in agriculture: A survey.”, Computers and Electronics in Agriculture, 147:70-90, (2018).
16. [16] Diffie, W., Hellman, M., “New directions in cryptography.”, IEEE transactions on Information Theory, 22(6):644-654, (1976).
17. [17] Dhiman, K., Kasana, S. S., “Extended visual cryptography techniques for true color images.”, Computers & Electrical Engineering, 70:647-658, (2018).
18. [18] Solak, S , Altınışık, U., “A new approach for Steganography: Bit shifting operation of encrypted data in LSB (SED-LSB).”, Bilişim Teknolojileri Dergisi, 12(1):75-81, (2019).
19. [19] Johnson, N. F., Jajodia, S., “Exploring steganography: Seeing the unseen.”, Computer, 31(2), (1998).
20. [20] Hussain, M., Wahab, A. W. A., Idris, Y. I. B., Ho, A. T., Jung, K. H., “Image steganography in spatial domain: A survey.”, Signal Processing: Image Communication, 65:46-66, (2018).
21. [21] Solak, S , Altınışık, U ., “The Least Significant Two-Bit Substitution Algorithm For Image Steganography.”, International Journal of Computer (IJC), 31(1):150-156, (2018).
22. [22] Solak, S., Altınışık, U., “LSB Substitution and PVD performance analysis for image steganography.”, International Journal of Computer Sciences and Engineering, 6(10):1-4, (2018).
23. [23] Petitcolas, F. A., Anderson, R. J.,Kuhn, M. G., “Information hiding-a survey.”, Proceedings of the IEEE, 87(7):1062-1078, (1999).
24. [24] Cheddad, A., Condell, J., Curran, K., Mc Kevitt, P., “Digital image steganography: Survey and analysis of current methods.”, Signal processing, 90(3):727-752, (2010).
25. [25] Bender, W., Gruhl, D., Morimoto, N., Lu, A., “Techniques for data hiding.”, IBM systems journal, 35(3.4):313-336, (1996).
26. [26] Solak, S., Altınışık, U., “Image steganography based on LSB substitution and encryption method: adaptive LSB+ 3.”, Journal of Electronic Imaging, 28(4):043025, (2019).
27. [27] Walia, G. S., Makhija, S., Singh, K., Sharma, K., “Robust stego-key directed LSB substitution scheme based upon cuckoo search and chaotic map.”, Optik, 170:106-124, (2018).
28. [28] Ibanez, A. L., Djamal, E. C., Ilyas, R., Najmurrokhman, A., “Optimization of Least Significant Bit Steganography Using Genetic Algorithm to Improve Data Security.”, In 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE). IEEE, 523-528, (2018).
29. [29] Shreelekshmi, R., Wilscy, M., Madhavan, C. V., “Undetectable least significant bit replacement steganography.”, Multimedia Tools and Applications, 1-18, (2018).
30. [30] Wu, D. C., Tsai, W. H., “A steganographic method for images by pixel-value differencing.”, Pattern Recognition Letters, 24(9-10):1613-1626, (2003).
31. [31] Wang, C. M., Wu, N. I., Tsai, C. S., Hwang, M. S., “A high quality steganographic method with pixel-value differencing and modulus function.”, Journal of Systems and Software, 81(1):150-158, (2008).
32. [32] Chen, J., “A PVD-based data hiding method with histogram preserving using pixel pair matching.”, Signal Processing: Image Communication, 29(3):375-384, (2014).
33. [33] Swain, G., “Adaptive pixel value differencing steganography using both vertical and horizontal edges.”, Multimedia Tools and Applications, 75(21):13541-13556, (2016).
34. [34] Hussain, M., Wahab, A. W. A., Ho, A. T., Javed, N., Jung, K. H., “A data hiding scheme using parity-bit pixel value differencing and improved rightmost digit replacement.”, Signal Processing: Image Communication, 50:44-57, (2017).
35. [35] Prasad, S., Pal, A. K., “An RGB colour image steganography scheme using overlapping block-based pixel-value differencing.”, Royal Society open science, 4(4):161066, (2017).
36. [36] Li, Z., He, Y., “Steganography with pixel-value differencing and modulus function based on PSO.”, Journal of information security and applications, 43:47-52, (2018).
37. [37] Plants Database, “The National Gardening Association”, https://garden.org/plants/group/, (accessed on February 2020).
38. [38] Turkomp, “Turkish Food Composition Database”, http://www.turkomp.gov.tr/main, (accessed on February 2020), (2013).
39. [39] Eurofir, http://www.eurofir.org/food-information/food-composition-databases/ (accessed on February 2020).
40. [40] Biringen Löker, G., Amoutzopoulos, B., Özge Özkoç, S., Özer, H., Şatir, G., Bakan, A., “A pilot study on food composition of five Turkish traditional foods.”, British Food Journal, 115(3):394-408, (2013).
41. [41] Kocak, C., “Clsm: Couple Layered Security Model A High-Capacity Data Hiding Scheme Using With Steganography.”, Image Analysis & Stereology, 36(1):15-23, (2017).
42. [42] Jung, K. H., “Data hiding scheme improving embedding capacity using mixed PVD and LSB on bit plane.”, Journal of Real-Time Image Processing, 14(1):127-136, (2018).
43. [43] Wang, Z., Bovik, A. C., Sheikh, H. R., Simoncelli, E. P., “Image quality assessment: from error visibility to structural similarity.”, IEEE transactions on image processing, 13(4):600-612, (2004).