KaydedilenlerDerginizi ekleyin

Dergiler / Gazi University Journal of Science / 2021 / Cilt: 34 - Sayı: 3

Image Steganography-Based GUI Design to Hide Agricultural Data

Serdar SOLAK
Umut ALTINIŞIK
Sayfa
748–763
Yayın Tarihi
2022-11-07
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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).

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Throughout the ages, safely preserving and transmitting data that have extraordinary importance for humanity has increased its importance with rapid advances in computer technology.
📊
Temel Bulgular & Veri:Steganography stores hidden data within the files, which are unnoticed by third parties, so it provides secure transmission of data to the receiver.
💡
Sonuç & Katkı: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).
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Image steganography · LSB substitution · PVD

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).

🔖

Bu Makaleye Atıf Yap

SOLAK, S., ALTINIŞIK, U. (2022). Image Steganography-Based GUI Design to Hide Agricultural Data. Gazi University Journal of Science, Cilt: 34 - Sayı: 3, 748–763. https://ajindex.com/articles/650879

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (12)

  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. 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. 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. 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. 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. 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. 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. 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. 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. Beyaz, A., Özkaya, M. T., İçen, D., “Identification of some spanish olive cultivars using image processing techniques.”, Scientia Horticulturae, 225:286-292, (2017)
  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. Kale, A. P., Sonavane, S. P., “IoT based Smart Farming: Feature subset selection for optimized high-dimensional data using improved GA based