Image Steganography Using Deep Learning Techniques

Digital image steganography is the process of embedding information withina cover image in  a  secure,  imperceptible,  and  recoverable  way.The  three  main  methods  of  digital  image steganography  are  spatial,  transform,  and  neural  network  methods. Spatial  methods  modify  the pixel  valuesof  an  image  to  embed  information,  while  transform  methods embed  hidden information within the frequency of the image.Neural network-based methods use neural networks to perform the hiding process, which is the focus of the proposed methodology.

This  research  explores  the  use  of  deep  convolutional  neural  networks  (CNNs)  in  digital image steganography. This work extends an existing implementation that used a two-dimensional CNN to perform the preparation, hiding, and extraction phases of the steganography process. The methodology proposed in this research, however, introduced changes into the structure of the CNN and   used   a   gain   function   based   on   several   image   similarity   metrics   to   maximize   the imperceptibility between a cover and steganographic image.

The  performance  of  the  proposed  method  was  measuredusing  some  frequently  utilized image metrics such as structured similarity index measurement (SSIM), mean square error (MSE), and  peak  signal  to  noise  ratio  (PSNR).  The  results  showed  that  the  steganographic  images produced by the proposed methodology areimperceptible to the human eye, while still providing good  recoverability.  Comparingthe  results  of  the  proposed  methodologyto  theresults  of  theoriginalmethodologyrevealed  that  our  proposed  network  greatly  improved  over  the  base methodology in terms of SSIM andcompareswell to existing steganography methods.