@Article{cmes.2020.09463,
AUTHOR = {Xintao Duan, Daidou Guo, Chuan Qin},
TITLE = {Image Information Hiding Method Based on Image Compression and Deep Neural Network},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {124},
YEAR = {2020},
NUMBER = {2},
PAGES = {721--745},
URL = {http://www.techscience.com/CMES/v124n2/39547},
ISSN = {1526-1506},
ABSTRACT = {Image steganography is a technique that hides secret information into the cover
image to protect information security. The current image steganography is mainly to embed
a smaller secret image in an area such as a texture of a larger-sized cover image, which will
cause the size of the secret image to be much smaller than the cover image. Therefore, the
problem of small steganographic capacity needs to be solved urgently. This paper proposes
a steganography framework that combines image compression. In this framework, the Vector Quantized Variational AutoEncoder (VQ-VAE) is used to achieve the compression of
the secret image. The compressed and reconstructed image is visually indistinguishable
from the original image and facilitates more embedded data information later. Finally,
the compressed image is transmitted to a SegNet deep neural network that contains a set
of encoders and decoders to achieve image hiding and extraction. Experimental results
show that the steganographic framework guarantees the quality of steganography while
its relative steganographic capacity reaches 1. Besides, Peak Signal-to-Noise Ratio (PSNR)
and Structural Similarity Index (SSIM) values can reach 42 dB and 0.94, respectively.},
DOI = {10.32604/cmes.2020.09463}
}