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StegaStamp using Pytorch

Contributers: Dan Epshtien and Neta Becker. Based on Jisong Xie's work

Goals:

  • Develop a tool that encrypts information into a natural image based on existing code from Berkeley university.
  • Improve the algorithm performance in order to receive optimal output images along with good decoding performance.

Notations:

  • Original image - the image before encoding
  • Encoded image - The image after encoding
  • Residual image - the image that is received by substracting the original image from the encoded image (Encoded image - Original image). meaning the values that were added to the original image during the encoding stage.

Results

Using different loss functions, we managed to receive those results

Left to right: residual image, encoded image, original image:

image

A few more examples (Left to right: residual image, encoded image, original image)

image image

More notations:

  • secret loss - The loss function of the encoding
  • decipher indicator - Graph that depicts the number of images the decoder managed to decipher out of each batch of 4 images

As seen in the graphs below, there is a trade-off between the two - if secret loss value is low than the decipher indicator is low (meaning we are able to decipher less images out of each batch) and vice versa. image