StegoGAN: Leveraging Steganography for Non-Bijective Image-to-Image
  Translation
Sidi Wu Yizi Chen Samuel Mermet Lorenz Hurni Konrad Schindler Nicolas Gonthier Loic Landrieu
Most image-to-image translation models postulate that a unique correspondence
exists between the semantic classes of the source and target domains. However,
this assumption does not always hold in real-world scenarios due to divergent
distributions, different class sets, and asymmetrical information
representation. As conventional GANs attempt to generate images that match the
distribution of the target domain, they may hallucinate spurious instances of
classes absent from the source domain, thereby diminishing the usefulness and
reliability of translated images. CycleGAN-based methods are also known to hide
the mismatched information in the generated images to bypass cycle consistency
objectives, a process known as steganography. In response to the challenge of
non-bijective image translation, we introduce StegoGAN, a novel model that
leverages steganography to prevent spurious features in generated images. Our
approach enhances the semantic consistency of the translated images without
requiring additional postprocessing or supervision. Our experimental
evaluations demonstrate that StegoGAN outperforms existing GAN-based models
across various non-bijective image-to-image translation tasks, both
qualitatively and quantitatively. Our code and pretrained models are accessible
at https://github.com/sian-wusidi/StegoGAN.