  mage steganography is the process of hiding information which can be text,
image, or video inside a cover image. The advantage of steganography over
cryptography is that the intended secret message does not attract attention and
is thus more suitable for secret communication in a highly-surveillant
environment such as civil disobedience movements. Internet memes in social
media and messaging apps have become a popular culture worldwide, so this folk
custom is a good application scenario for image steganography. We try to
explore and adopt the steganography techniques on the Internet memes in this
work. We implement and improve the HiDDeN model by changing the Conv-BN-ReLU
blocks convolution layer with a multiscale autoencoder network so that the
neural network learns to embed message bits in higher-level feature space.
Compared to methods that convolve feature filters on the row-pixel domain, our
proposed MS-Hidden network learns to hide secrets in both low-level and
high-level image features. As a result, the proposed model significantly
reduces the bit-error rate to empirically 0% and the required network
parameters are much less than the HiDDeN model.
