Medikal Görüntülerde Derin Öğrenme ile Steganaliz

Year 2021, Volume: 14 Issue: 2, 151 - 159, 30.04.2021

Rukiye Karakış , Kali Gurkahraman

https://doi.org/10.17671/gazibtd.799370

Cited By: 1

Abstract

Steganaliz ile bir medya dosyasındaki gizli mesajı elde etmek ya da sadece mesajın varlığını tespit etmek amaçlanır. Literatürde medikal verilerin güvenliğini sağlamayı amaçlayan pek çok steganografi yöntemi mevcut olsa da medikal steganaliz çalışması çok azdır. Bu çalışmada, medikal görüntü steganografi yöntemlerinin dayanıklılığının arttırılmasında kullanılabilecek ve medikal bir görüntüde gizli mesajların varlığını tespit edebilecek bir sınıflandırıcı geliştirilmesi amaçlanmıştır. Bunun için karmaşık ve maliyetli öznitelik analizine gerek duymayan bir derin öğrenme mimarisi olan evrişimsel sinir ağı(ESA) taşıyıcı ve stego medikal görüntüler ile eğitilmiş ve test edilmiştir. Doğruluk, kesinlik, hassasiyet ve F1 değerleri sırasıyla 0,964, 0,966, 0965 ve 0964 olarak elde edilmiştir. Bu çalışma, derin öğrenme yönteminin medikal görüntü steganalizinde de kullanılabileceğini ilk kez göstermiştir.

Keywords

derin öğrenme, evrişimsel sinir ağı, medikal görüntü steganalizi, medikal görüntü steganografi, uzamsal düzlem

Steganalysis with Deep Learning on Medical Images

Abstract

With steganalysis, it is aimed to obtain a hidden message from a media file or only to detect the presence of the message. Although there are many steganography methods in the literature aiming to ensure the medical data security, medical steganalysis studies are very few. In this study, it is aimed to develop a classifier that can be used to increase the durability of medical image steganography methods and detect the presence of hidden messages in a medical image. For this, the convolutional neural network (CNN), a deep learning architecture that does not require complex and costly feature analysis, has been trained and tested with cover and stego medical images. Accuracy, precision, recall and F1 values were obtained as 0.964, 0.966, 0965 and 0964, respectively. This study has shown for the first time that the deep learning method can also be used in medical image steganalysis.

Keywords

deep learning, convolutional neural network, medical image steganalysis, medical image steganography, spatial transform

References

• M. Salomon, R. Couturier, C. Guyeux, J.-F. Couchot, J.M. Bahi, “Steganalysis via a Convolutional Neural Network Using Large Convolution Filters for Embedding Process with Same Stego Key: A Deep Learning Approach For Telemedicine”, European Research in Telemedicine/La Recherche Européenne en Télémédecine, 6, 79-92, 2017.
• K. Karampidis, E. Kavallieratou, G. Papadourakis, “A Review of Image Steganalysis Techniques for Digital Forensics”, Journal of Information Security and Applications, 40, 217-235, 2018.
• M. Bilgin, “Steganaliz”, Akademik Bilişim’14 - XVI. Akademik Bilişim Konferansı Bildirileri, Mersin Üniversitesi, Mersin, 693-698, 2014. J. Fridrich, M. Goljan, R. Du, “Reliable detection of LSB steganography in color and grayscale images”, Proceedings of the 2001 workshop on multimedia and security new challenges - (MM&Sec ’01), 27, 2001.
• I. Avcibas, N. Memon, B. Sankur, Steganalysis Based on Image Quality Metrics, 2001 IEEE Fourth Workshop on Multimedia Signal Processing, 517-522, 2001.
• I. Avcıbas, B. Sankur, K. Sayood, “Statistical evaluation of image quality measures”, Journal of Electronic Imaging, 11(2), 206-223, 2002.
• R. Karakis, I. Güler, I. Capraz, E. Bilir, “A novel fuzzy logic based image steganography method to ensure medical data security”, Computers in Biology and Medicine, 67, 172-183, 2015.
• R. Karakis, I. Guler, “Steganography and Medical Data Security”, Cryptographic and Information Security Approaches for Images and Videos, Cilt 22, Editor: S. Ramakrishnan, CRC Press, USA, ISBN: 9781138563841, 627-660, 2019.
• M. Chaumont, “Deep Learning in steganography and steganalysis from 2015 to 2018”, Digital Media Steganography: Principles, Algorithms, Advances, Editor: M. Hassaballah, Elsevier Inc, 1-45, 2020.
• Y. Qian, J. Dong, W. Wang, T. Tan, “Deep learning for steganalysis via convolutional neural networks,” Proc. SPIE 9409, Media Watermarking, Security, and Forensics 2015, 94090J, 2015.
• Y. Qian, J. Dong, W. Wang, T. Tan, “Learning and Transferring Representations for Image Steganalysis Using Convolutional Neural Network”, 2016 IEEE International Conference on Image Processing (ICIP), 2752-2756, 2016.
• G. Xu, H.-Z. Wu, Y.-Q. Shi, “Structural Design of Convolutional Neural Networks for Steganalysis”, IEEE Signal Process. Lett., 23(5), 708-712, 2016.
• G. Xu, H.-Z. Wu, Y.-Q. Shi, “Ensemble of CNNs for steganalysis: An empirical study”, Proc. 4th ACM Workshop Inf. Hiding Multimedia Secur., 103-107, 2016.
• J. Kodovsky, J. Fridrich, V. Holub, “Ensemble classifiers for steganalysis of digital media”, IEEE Transactions on Information Forensics and Security, 7(2), 432-444, 2012.
• K. Liu, J. Yang, X. Kang, “Ensemble of CNN and rich model for steganalysis”, 2017 International Conference on Systems, Signals and Image Processing (IWSSIP), Poznan, 1-5, 2017.
• J. Ye, J. Ni, Y. Yi, “Deep Learning Hierarchical Representations for Image Steganalysis”, IEEE Transactions on Information Forensics And Security, 12(11), 2545-2557, 2017.
• M. Sharifzadeh, C. Agarwal, M. Aloraini, D. Schonfeld, “Convolutional neural network steganalysis’s application to steganography”, 2017 IEEE Visual Communications and Image Processing(VCIP), 1-4, 2017.
• S. Ozcan, A.F. Mustacoglu, “Transfer Learning Effects on Image Steganalysis with Pre-Trained Deep Residual Neural Network Model”, 2018 IEEE International Conference on Big Data (Big Data), 2280-2287, 2018.
• S. Wu, S. Zhong, Y. Liu, “Deep residual learning for image steganalysis”. Multimed Tools Appl., 77, 10437-10453, 2018.
• W. You, X. Zhao, S. Ma, Y. Liu, “RestegNet: a residual steganalytic network”, Multimed Tools Appl., 78, 22711-22725, 2019.
• Internet: Figshare brain tumor dataset, https://doi.org/10.6084/m9.figshare.1512427.v5., 21.09.2020.
• F.B. Maroof Ozcan, R. Karakis, I. Guler, “Medikal Görüntüler Üzerinde Destek Vektör Makinesi ile Steganaliz, The 28th IEEE Conference on Signal Processing And Communications Applications, Gaziantep, 2020.
• I. Goodfellow, Y. Bengio, A. Courville, Deep learning (Adaptive computation and machine learning), The MIT Press, Cambridge, Massachusetts, 2016.
• K. Gurkahraman, R., Karakis, Brain Tumors Classification with Deep Learning using Data Augmentation, Journal of the Faculty of Engineering and Architecture of Gazi University, 36 (2), 997-1011, 2021.
• M. Yapıcı, A. Tekerek, N. Topaloğlu, “Literature Review of Deep Learning Research Areas”, Gazi Mühendislik Bilimleri Dergisi (GMBD), 5(3), 188-215, 2019.
• A. Krizhevsky, I. Sutskever, I., G. Hinton, “ImageNet classification with deep convolutional neural networks”, NIPS'12 Proceedings of the 25th International Conference on Neural Information Processing Systems, 1, 1097-1105, 2012.
• R. Yamashita, M. Nishio, R.K.G. Do, K. Togashi, “Convolutional neural networks: an overview and application in radiology”, Insights Imaging, 9, 611-629, 2018.
• J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, G. Wang, J. Cai, T. Chen, “Recent advances in convolutional neural networks”, Pattern Recognition, 77, 354-377, 2018.
• Y.A. LeCun, L. Bottou, G.B. Orr, K.-R. Muller, “Efficient backprop”, Neural Networks: Tricks of the Trade-Second Edition, 9-48, 2012.
• V. Nair, G.E. Hinton, “Rectified linear units improve restricted boltzmann machines”, Proceedings of the International Conference on Machine Learning (ICML), 807-814, 2010.
• M. Ayyüce Kızrak ve B. Bolat, “Derin Öğrenme ile Kalabalık Analizi Üzerine Detaylı Bir Araştırma”, Bilişim Teknolojileri Dergisi, 11(3), 263-286, 2018.
• A. Khan, A. Sohail, U. Zahoora, U., A.S. Qureshi, “A survey of the recent architectures of deep convolutional neural networks”, Artificial Intelligence Review, 53, 5455-5516, 2020.
• J. Nalepa, M. Marcinkiewicz, M. Kawulok, Data Augmentation for Brain-Tumor Segmentation: A Review, Frontiers in Computational Neuroscience, 13(83), 1-18. 2019.
• R. Karakis, M. Tez, Y.A. Kilic, B. Kuru, I. Guler, “A genetic algorithm model based on artificial neural network for prediction of the axillary lymph node status in breast cancer”, Engineering Applications of Artificial Intelligence, 26(3), 945-950, 2013.
• Internet: Precision and recall, https://en.wikipedia.org/wiki/Precision_and_recall, 21.09.2020.
• Q. Liu, “Steganalysis of DCT-Embedding Based Adaptive Steganography and YASS”, Proceedings of the 13th ACM Multimedia & Security Workshop, Niagara Falls, NY, 77-86, 2011.