Akademik Veri Yönetim Sistemi
International Conference on Engineering Technologies (ICENTE'21) , Konya, Türkiye, 18 - 20 Kasım 2021, ss.88-92
Image steganography ensures that secret data
is hidden in a cover image and aims to transmit the resulting stego image
through a communication channel without being noticed by a third party. On the
other hand, image steganalysis detects the hidden data in the stego images.
Traditional steganalysis techniques focus on obtaining hidden data. However,
the presence of secret data must be revealed before obtaining it. Machine Learning
(ML) classifiers are used for this purpose with promising high-performance
values. ML techniques other than deep learning (DL) require complex and costly
feature analysis performed in spatial or transform space. In recent years, DL
models have been used to detect the presence of secret messages in the BOSSBase
dataset, but there is no study for medical image steganalysis. Therefore, this
study aimed to perform medical image steganalysis using a DL model that
performs feature analysis on its convolutional layers. An original medical
image dataset containing brain MR images was obtained from epileptic patients
and healthy volunteers. Two deep convolutional neural networks (CNN) were
used. One of them was trained without
transfer learning while the feature layers and weights of DenseNet, ResNet,
Inception, and Efficient models were transferred to the other one. The training data was obtained by hiding the
secret data to the brain images, with different capacity ratios between 0.1 and
1.0 bit per pixel (bpp) using the WOW technique. The results can be summarized
in two aspects. First, as expected, the higher the capacity ratio was, the
higher classification performance it was obtained. Second, using transfer
learning increased the classification performance of the DL model.