Steganalysis for Reversible Data Hiding Based on Neural Networks and Convolutional Neural Networks
Journal Title: International Journal of Machine Learning and Networked Collaborative Engineering - Year 2018, Vol 2, Issue 2
Abstract
Lossless data hiding techniques is a technique that is very interesting. In which there is a large amount of reversible information hidden technologies. This technique makes it possible to restore the original image after extracting the information from the stego image. The stego image (hidden image with secret data) is hardly detected by any variable. There are many studies for this field are published. Secret information is hidden on the pixel space, frequency (cosine, wavelet) coefficient space or difference image coefficient space. However, by analyzing meticulously between the cover image and the stego image on these space, one can detect abnormal signs. In a previous work, a steganalytic techniques produced that was based on analysis of the transform coefficient histogram with the correct detection ratio between 88% and 92%. In this article, proposing another method to improve the detection ratio of that steganalysis based on Neural Networks (NNs) and Convolutional Neural Networks (CNNs). The test results show 96% correct detection rates for NNs and 94% for CNNs, this is a better result than our previous method. This proposed approach can be applied to detect stego images on spatial and other frequency domain.
Authors and Affiliations
Ho Thi Huong Thom
Keywords
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- EP ID EP371002
- DOI 10.30991/IJMLNCE.2018v02i02.001
- Views 124
- Downloads 0
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