Steganalysis for Reversible Data Hiding based on Neural Networks and Convolutional Neural Networks - Thom, Ho Thi Huong; Nguyen, Kim Anh; Vu, Bui Dinh (International Journal of Machine Learning and Networked Collaborative Engineering, 2018)
Lossless data hiding techniques is a technique that is very interested. In which there is a large amount of reversible information hidden technologies. This technique is technically possible to restore the original image after extracting the information from the stego image. The stego image (image to be hidden secret data) is not detected hardly any variable. There are many studies for this field is published. Secret information is hidden on the pixel space, frequency (cosine, wavelet) coefficient space or difference image coefficient space. However, by analysing meticulously between the cover image and the stego image on these space can be detect abnormal signs. In my previous work, we produced a steganalytic techniques based on analysing the transform coefficient histogram with the correct detection ratio between 88% and 92%. In this article, my team give another method to improve the detection ratio of that steganalysis based on Neural Networks (NNs) and Convolutional Neural Networks (CNNs). Our test results show 94% correct detection rates for NNs and 93% 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.