Steganography Applications of StyleGAN: A Short Analytical Investigation from Hiding Message in Face Image

In this investigation, we delve into the latent codes denoted as w, pertaining to both original and encoded images in steganography models,
which are projected through StyleGAN—a generative adversarial network renowned for generating aesthetic synthesis. We present evidence
of disentanglement and latent code alterations between the original and
encoded images. This investigator possesses the potential to assist in the
concealment of messages within images through the manipulation of latent codes within the original images, resulting in the generation of encoded images. The message into encoded renderings is facilitated by the
employment of CodeFace, serving as a steganography model. CodeFace
comprises an encoder and decoder architecture wherein the encoder conceals a message within an image, while the decoder retrieves the message
from the encoded image. By gauging the average disparities amid the
latent codes belonging to the original and encoded images, a discerning
revelation of optimal channels for concealing information comes to light.
Precisely orchestrated manipulation of these channels furnishes us with
the means to engender novel encoded visual compositions.