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Seçilen steganografi yöntemlerinin derin öğrenme modelleri ile steganaliz performansının incelenmesi

Year 2025, Volume: 40 Issue: 1, 443 - 454, 16.08.2024
https://doi.org/10.17341/gazimmfd.1324765

Abstract

Görünüşte zararsız dijital ortamlarda bilgi gizleme sanatı olan steganografi, bilgi güvenliği için önemli bir sorun teşkil etmektedir. Son yıllarda, derin öğrenme teknikleri, çeşitli bilgisayarla görme görevleri için güçlü araçlar olarak ortaya çıkmıştır. Bu makale, dijital ortamdan gizli bilgilerin saptanması ve çıkarılmasına odaklanan, derin öğrenmeye dayalı steganalizdeki en son teknolojinin kapsamlı bir incelemesini sunar. Steganografinin altında yatan kavramlar ve sonuçları araştırıldı, ardından bu çalışmada steganalizde kullanılan farklı derin öğrenme mimarileri ve metodolojileri keşfedilmiştir. Ayrıca, derin öğrenmeye dayalı steganaliz ile ilgili zorlukları ve sınırlamaları vurgulanılmış ve gelecekteki araştırmalar için potansiyel yollar önerilmiştir.

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References

  • 1. Kodovsky J.S., Sedighi V., Fridrich J., Study of Cover Source Mismatch in Steganalysis and Ways to Mitigate its Impact, Proc. SPIE 9028, Media Watermarking, Security, and Forensics 2014, San Francisco, California, USA, 2014.
  • 2. Sedighi V., Cogranne R., Fridrich J., Content-adaptive steganography by minimizing statistical detectability, IEEE Transactions on Information Forensics and Security, 11 (2), 221–234, 2016.
  • 3. Forouzan B.A., Introduction to Cryptography and Network Security, McGraw-Hill Higher Education, isbn: 978-0-07-287022-0, New York, USA, 2008.
  • 4. Fridrich J., Kodovsky J., Rich models for steganalysis of digital images, IEEE Transactions on Information Forensics and Security 7 (3), 868–882, 2012.
  • 5. Ye J., Ni J., Yi Y., Deep learning hierarchical representations for image steganalysis, IEEE Transactions on Information Forensics and Security, 12 (11), 2545–2557, 2017.
  • 6. Tompson J., Goroshin R., Jain A., LeCun Y., Bregler C., Efficient object localization using convolutional networks, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 648-656., 2015.
  • 7. Simonyan K., Andrew Z., Very Deep Convolutional Networks for Large-Scale Image Recognition, ICLR 2015, San Diego, USA, 2015.
  • 8. Pevny T., FillerT., Bas P., Using High-Dimensional Image Models to Perform Highly Undetectable Steganography., Information Hiding, Calgary, Canada, 161-177, 2010.
  • 9. Qian Y., Dong J., Wang W., Tan T., Deep learning for steganalysis via convolutional neural networks, Proc. SPIE 9409, Media Watermarking, Security, and Forensics 2015, San Francisco, California, USA, 2015.
  • 10. Xu G., Wu H., Shi Y.Q., Structural Design of Convolutional Neural Networks for Steganalysis, IEEE Signal Processing Letters, 23, 708-712, 2016.
  • 11. Xu G., Wu H., Shi Y.Q., Ensemble of CNNs for Steganalysis: An Empirical Study, IH&MMSec '16: Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security, New York, USA, 103-107, 2016.
  • 12. Yedroudj M., Comby F., Chaumont M., Yedroudj-Net: An Efficient CNN for Spatial Steganalysis, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada, 2092-2096, 2018.
  • 13. Tabares-Soto R., Arteaga H.B., Mora-Rubio A., Bravo-Ortíz M.A., Garzón D.A., Grisales J.A.A., Jacome A.B., Orozco-Arias S., Isaza G., Pollan R.R., Strategy to improve the accuracy of convolutional neural network architectures applied to digital image steganalysis in the spatial domain., PeerJ Computer Science, 2021.
  • 14. Fu T., Chen L., Fu Z., Yu K., Wang Y., CCNet: CNN model with channel attention and convolutional pooling mechanism for spatial image steganalysis, Journal of Visual Communication and Image Representation, 88, 2022.
  • 15. Buluş E., Gender Determination from Pictures with CNN Models, 2021 6th International Conference on Computer Science and Engineering (UBMK), Ankara, Turkey, 310-313, 2021.
  • 16. Bas P., Filler T., Pevny T., Break our steganographic system: the ins and outs of organizing BOSS, Information Hiding IH 2011, Lecture Notes in Computer Science, 6958, 59–70, 2011.
  • 17. Mazurczyk W., Wendzel S., Information Hiding: Challenges for Forensic Experts, Communications of the ACM. 61, 86-94, 2017.
  • 18. Zhang R., Zhu F., Liu J., Liu G., Depth-Wise Separable Convolutions and Multi-Level Pooling for an Efficient Spatial CNN-Based Steganalysis, in IEEE Transactions on Information Forensics and Security, 15, 1138-1150, 2020.
  • 19. Holub V., Fridrich J., Designing steganographic distortion using directional filters, In: IEEE International Workshop on Information Forensics and Security 2012, 234–239, 2012.
  • 20. Binghamton University, Steganographic algorithms, http://dde.binghamton edu/download/stego_algorith ms/, yayın tarihi 2015, Erişim tarihi Temmuz 2023.
  • 21. Boroumand M., Fridrich J., Synchronizing Embedding Changes in Side-Informed Steganography, Proc. IS&T Int’l. Symp. on Electronic Imaging: Media Watermarking, Security, and Forensics, 290, 1-12, 2020.
  • 22. Holub V., Fridrich J., Denemark T., Universal distortion function for steganography in an arbitrary domain. EURASIP Journal on Information Security, 2014.
  • 23. Li B., Wang M., Huang J, Li X., A new cost function for spatial image steganography, In: 2014 IEEE International Conference on Image Processing (ICIP). Piscataway: IEEE, 4206–4210, 2014.
  • 24. StanfordVisionLab, ImageNet Large Scale Visual Recognition Challenge 2014 (ILSVRC2014), https://www.image-net.org/challenges/LSVRC/2014/, yayın tarihi 2014, Erişim tarihi Temmuz 2023.
  • 25. Karahanlı G., Taşkın C., Determining the growth stages of sunflower plants using deep learning methods, Journal of the Faculty of Engineering and Architecture of Gazi University, 39(3), 1455-1472, 2024.
  • 26. Kadiroğlu Z., Deniz E., Şenyiğit A., A comparison of deep learning models for pneumonia detection from chest x-ray images, Journal of the Faculty of Engineering and Architecture of Gazi University, 39 (2), 729-740, 2023.
Year 2025, Volume: 40 Issue: 1, 443 - 454, 16.08.2024
https://doi.org/10.17341/gazimmfd.1324765

Abstract

Project Number

yoktur

References

  • 1. Kodovsky J.S., Sedighi V., Fridrich J., Study of Cover Source Mismatch in Steganalysis and Ways to Mitigate its Impact, Proc. SPIE 9028, Media Watermarking, Security, and Forensics 2014, San Francisco, California, USA, 2014.
  • 2. Sedighi V., Cogranne R., Fridrich J., Content-adaptive steganography by minimizing statistical detectability, IEEE Transactions on Information Forensics and Security, 11 (2), 221–234, 2016.
  • 3. Forouzan B.A., Introduction to Cryptography and Network Security, McGraw-Hill Higher Education, isbn: 978-0-07-287022-0, New York, USA, 2008.
  • 4. Fridrich J., Kodovsky J., Rich models for steganalysis of digital images, IEEE Transactions on Information Forensics and Security 7 (3), 868–882, 2012.
  • 5. Ye J., Ni J., Yi Y., Deep learning hierarchical representations for image steganalysis, IEEE Transactions on Information Forensics and Security, 12 (11), 2545–2557, 2017.
  • 6. Tompson J., Goroshin R., Jain A., LeCun Y., Bregler C., Efficient object localization using convolutional networks, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 648-656., 2015.
  • 7. Simonyan K., Andrew Z., Very Deep Convolutional Networks for Large-Scale Image Recognition, ICLR 2015, San Diego, USA, 2015.
  • 8. Pevny T., FillerT., Bas P., Using High-Dimensional Image Models to Perform Highly Undetectable Steganography., Information Hiding, Calgary, Canada, 161-177, 2010.
  • 9. Qian Y., Dong J., Wang W., Tan T., Deep learning for steganalysis via convolutional neural networks, Proc. SPIE 9409, Media Watermarking, Security, and Forensics 2015, San Francisco, California, USA, 2015.
  • 10. Xu G., Wu H., Shi Y.Q., Structural Design of Convolutional Neural Networks for Steganalysis, IEEE Signal Processing Letters, 23, 708-712, 2016.
  • 11. Xu G., Wu H., Shi Y.Q., Ensemble of CNNs for Steganalysis: An Empirical Study, IH&MMSec '16: Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security, New York, USA, 103-107, 2016.
  • 12. Yedroudj M., Comby F., Chaumont M., Yedroudj-Net: An Efficient CNN for Spatial Steganalysis, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada, 2092-2096, 2018.
  • 13. Tabares-Soto R., Arteaga H.B., Mora-Rubio A., Bravo-Ortíz M.A., Garzón D.A., Grisales J.A.A., Jacome A.B., Orozco-Arias S., Isaza G., Pollan R.R., Strategy to improve the accuracy of convolutional neural network architectures applied to digital image steganalysis in the spatial domain., PeerJ Computer Science, 2021.
  • 14. Fu T., Chen L., Fu Z., Yu K., Wang Y., CCNet: CNN model with channel attention and convolutional pooling mechanism for spatial image steganalysis, Journal of Visual Communication and Image Representation, 88, 2022.
  • 15. Buluş E., Gender Determination from Pictures with CNN Models, 2021 6th International Conference on Computer Science and Engineering (UBMK), Ankara, Turkey, 310-313, 2021.
  • 16. Bas P., Filler T., Pevny T., Break our steganographic system: the ins and outs of organizing BOSS, Information Hiding IH 2011, Lecture Notes in Computer Science, 6958, 59–70, 2011.
  • 17. Mazurczyk W., Wendzel S., Information Hiding: Challenges for Forensic Experts, Communications of the ACM. 61, 86-94, 2017.
  • 18. Zhang R., Zhu F., Liu J., Liu G., Depth-Wise Separable Convolutions and Multi-Level Pooling for an Efficient Spatial CNN-Based Steganalysis, in IEEE Transactions on Information Forensics and Security, 15, 1138-1150, 2020.
  • 19. Holub V., Fridrich J., Designing steganographic distortion using directional filters, In: IEEE International Workshop on Information Forensics and Security 2012, 234–239, 2012.
  • 20. Binghamton University, Steganographic algorithms, http://dde.binghamton edu/download/stego_algorith ms/, yayın tarihi 2015, Erişim tarihi Temmuz 2023.
  • 21. Boroumand M., Fridrich J., Synchronizing Embedding Changes in Side-Informed Steganography, Proc. IS&T Int’l. Symp. on Electronic Imaging: Media Watermarking, Security, and Forensics, 290, 1-12, 2020.
  • 22. Holub V., Fridrich J., Denemark T., Universal distortion function for steganography in an arbitrary domain. EURASIP Journal on Information Security, 2014.
  • 23. Li B., Wang M., Huang J, Li X., A new cost function for spatial image steganography, In: 2014 IEEE International Conference on Image Processing (ICIP). Piscataway: IEEE, 4206–4210, 2014.
  • 24. StanfordVisionLab, ImageNet Large Scale Visual Recognition Challenge 2014 (ILSVRC2014), https://www.image-net.org/challenges/LSVRC/2014/, yayın tarihi 2014, Erişim tarihi Temmuz 2023.
  • 25. Karahanlı G., Taşkın C., Determining the growth stages of sunflower plants using deep learning methods, Journal of the Faculty of Engineering and Architecture of Gazi University, 39(3), 1455-1472, 2024.
  • 26. Kadiroğlu Z., Deniz E., Şenyiğit A., A comparison of deep learning models for pneumonia detection from chest x-ray images, Journal of the Faculty of Engineering and Architecture of Gazi University, 39 (2), 729-740, 2023.
There are 26 citations in total.

Details

Primary Language Turkish
Subjects Deep Learning, Information Security and Cryptology, Cryptography
Journal Section Makaleler
Authors

Ercan Buluş 0000-0001-9442-6253

Project Number yoktur
Early Pub Date July 1, 2024
Publication Date August 16, 2024
Submission Date July 9, 2023
Acceptance Date April 8, 2024
Published in Issue Year 2025 Volume: 40 Issue: 1

Cite

APA Buluş, E. (2024). Seçilen steganografi yöntemlerinin derin öğrenme modelleri ile steganaliz performansının incelenmesi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 40(1), 443-454. https://doi.org/10.17341/gazimmfd.1324765
AMA Buluş E. Seçilen steganografi yöntemlerinin derin öğrenme modelleri ile steganaliz performansının incelenmesi. GUMMFD. August 2024;40(1):443-454. doi:10.17341/gazimmfd.1324765
Chicago Buluş, Ercan. “Seçilen Steganografi yöntemlerinin Derin öğrenme Modelleri Ile Steganaliz performansının Incelenmesi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 40, no. 1 (August 2024): 443-54. https://doi.org/10.17341/gazimmfd.1324765.
EndNote Buluş E (August 1, 2024) Seçilen steganografi yöntemlerinin derin öğrenme modelleri ile steganaliz performansının incelenmesi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 40 1 443–454.
IEEE E. Buluş, “Seçilen steganografi yöntemlerinin derin öğrenme modelleri ile steganaliz performansının incelenmesi”, GUMMFD, vol. 40, no. 1, pp. 443–454, 2024, doi: 10.17341/gazimmfd.1324765.
ISNAD Buluş, Ercan. “Seçilen Steganografi yöntemlerinin Derin öğrenme Modelleri Ile Steganaliz performansının Incelenmesi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 40/1 (August 2024), 443-454. https://doi.org/10.17341/gazimmfd.1324765.
JAMA Buluş E. Seçilen steganografi yöntemlerinin derin öğrenme modelleri ile steganaliz performansının incelenmesi. GUMMFD. 2024;40:443–454.
MLA Buluş, Ercan. “Seçilen Steganografi yöntemlerinin Derin öğrenme Modelleri Ile Steganaliz performansının Incelenmesi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 40, no. 1, 2024, pp. 443-54, doi:10.17341/gazimmfd.1324765.
Vancouver Buluş E. Seçilen steganografi yöntemlerinin derin öğrenme modelleri ile steganaliz performansının incelenmesi. GUMMFD. 2024;40(1):443-54.