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.

翻译：大多数图像到图像翻译模型假设源域与目标域的语义类别之间存在唯一的对应关系。然而，由于分布差异、类别集不同以及信息表征不对称，这一假设在现实场景中并不总是成立。传统GAN试图生成匹配目标域分布的图像，可能虚构出源域中不存在的类别实例，从而降低翻译图像的实用性和可靠性。基于CycleGAN的方法也被发现会将不匹配的信息隐藏到生成图像中以绕过循环一致性目标，这一过程被称为隐写术。针对非双射图像翻译的挑战，我们提出StegoGAN——一种利用隐写术防止生成图像中出现虚假特征的新模型。该方法在不需额外后处理或监督的情况下增强翻译图像的语义一致性。实验评估表明，StegoGAN在多种非双射图像到图像翻译任务中，无论定性还是定量均优于现有基于GAN的模型。我们的代码与预训练模型可在https://github.com/sian-wusidi/StegoGAN获取。