StegoGAN: Leveraging Steganography for Non-Bijective Image-to-Image Translation
(2403.20142)Abstract
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.
Overview
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StegoGAN introduces a new model for image-to-image translation that addresses the issue of non-bijective translation using steganography to enhance semantic consistency.
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It differentiates itself from existing models by explicitly disentangling matchable and unmatchable information during the translation process to reduce hallucinated elements and maintain semantic integrity.
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The model builds on the CycleGAN architecture, incorporating an unmatchability mask to segregate content and modulate translation cycles for improved semantic fidelity.
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Empirical evaluations demonstrate StegoGAN's superior performance in various domains, outperforming existing GAN-based frameworks in preserving semantic content and reducing artifacts.
StegoGAN: A Novel Approach to Handle Non-Bijective Image-to-Image Translation with Steganography
Introduction
Image-to-image translation frameworks have predominantly relied on the assumption that there exists a one-to-one correspondence between the semantic classes across source and target domains. This bijectivity assumption, however, fails to encapsulate the complexity and diversity encountered in real-world applications. For instance, certain classes present in the target domain may lack equivalents in the source domain, leading standard generative adversarial networks (GANs) to hallucinate or invent features, thereby compromising the fidelity and utility of the generated images. Addressing this issue, this blog post introduces StegoGAN, a model that innovatively uses steganography to mitigate the challenges posed by non-bijective image translation. It accomplishes this by enhancing semantic consistency without the need for explicit post-processing or additional supervisory signals.
Novel Contributions
StegoGAN presents several key contributions to the field of image-to-image translation. It identifies and directly tackles the limitations of existing models in dealing with unmatchable classes across translation domains. Unlike conventional methods, which might generate spurious features in an attempt to match the target distribution, StegoGAN employs steganography to explicitly disentangle matchable and unmatchable information during the image translation process. This methodology significantly reduces the occurrence of hallucinated elements in the generated images, thereby ensuring a higher level of semantic integrity.
Methodology
StegoGAN extends upon the CycleGAN architecture, introducing a novel mechanism to segregate matchable from unmatchable content through an unmatchability mask. This mask is instrumental in preventing the generator from incorporating features of classes that do not have counterparts in the source domain. The proposed model employs a two-pronged approach, modulating the forward and backward translation cycles to ensure that only matchable content influences the generation process. Specifically, the backward cycle decodes only the matchable information to reconstruct the input image, while the forward cycle leverates the unmatchability mask to guide the generation toward semantic fidelity.
Experimental Evaluation
StegoGAN's efficacy is demonstrated across various image-to-image translation tasks encompassing divergent domains such as cartography, natural scenery translation, and medical imaging. The model outperforms existing GAN-based frameworks both qualitatively and quantitatively, particularly in the preservation of semantic content and the elimination of artifact generation. Key metrics such as RMSE (Root Mean Square Error), FID (Fréchet Inception Distance), and other domain-specific measures are used to quantify the improvements over state-of-the-art methods. Additionally, data from open-access sources is utilized to empirically validate the model's performance and its robustness in handling non-bijective translation scenarios.
Implications and Future Directions
StegoGAN's innovative use of steganography to address the challenge of non-bijective image translation has far-reaching implications for various applications, including but not limited to medical imaging, autonomous driving, and geographic information systems. By ensuring the semantic integrity of translated images, StegoGAN represents a significant step forward in the development of more reliable and accurate image translation models.
Looking ahead, it would be interesting to explore the integration of StegoGAN's methodology with other types of generative models, such as Variational Autoencoders (VAEs) or diffusion models, to further enhance the quality and semantic consistency of translated images. Additionally, refining the unmatchability mask to achieve even greater precision in distinguishing between matchable and unmatchable content could open up new avenues for research in unsupervised domain adaptation and cross-domain understanding.
In conclusion, StegoGAN emerges as a pivotal development in the realm of image-to-image translation, paving the way for future investigations and applications that demand higher levels of semantic fidelity and reliability in generated images.
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