  Recently, the field of steganography has experienced rapid developments based
on deep learning (DL). DL based steganography distributes secret information
over all the available bits of the cover image, thereby posing difficulties in
using conventional steganalysis methods to detect, extract or remove hidden
secret images. However, our proposed framework is the first to effectively
disable covert communications and transactions that use DL based steganography.
We propose a DL based steganalysis technique that effectively removes secret
images by restoring the distribution of the original images. We formulate a
problem and address it by exploiting sophisticated pixel distributions and an
edge distribution of images by using a deep neural network. Based on the given
information, we remove the hidden secret information at the pixel level. We
evaluate our technique by comparing it with conventional steganalysis methods
using three public benchmarks. As the decoding method of DL based steganography
is approximate (lossy) and is different from the decoding method of
conventional steganography, we also introduce a new quantitative metric called
the destruction rate (DT). The experimental results demonstrate performance
improvements of 10-20% in both the decoded rate and the DT.
