StegoGAN: Leveraging Steganography for Non-Bijective Image-to-Image Translation

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 asymmet- rical 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 dimin- ishing the usefulness and reliability of translated images. CycleGAN-based methods are also known to hide the mis- matched information in the generated images to bypass cy- cle consistency objectives, a process known as steganogra- phy. 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 ad- ditional 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 quantita- tively. Our code and pretrained models are accessible at https://github.com/sian-wusidi/StegoGAN.