mlsanityData to 14 Oct 2026

StegOT: Trade-offs in Steganography via Optimal Transport

arXiv 2509.11178 ↗First posted 14 Sep 2025cs.CV cs.AIcited by 1

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Abstract (arXiv)

Image hiding is often referred to as steganography, which aims to hide a secret image in a cover image of the same resolution. Many steganography models are based on genera-tive adversarial networks (GANs) and variational autoencoders (VAEs). However, most existing models suffer from mode collapse. Mode collapse will lead to an information imbalance between the cover and secret images in the stego image and further affect the subsequent extraction. To address these challenges, this paper proposes StegOT, an autoencoder-based steganography model incorporating optimal transport theory. We designed the multiple channel optimal transport (MCOT) module to transform the feature distribution, which exhibits multiple peaks, into a single peak to achieve the trade-off of information. Experiments demonstrate that we not only achieve a trade-off between the cover and secret images but also enhance the quality of both the stego and recovery images. The source code will be released on https://github.com/Rss1124/StegOT.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 32 references that are in the map. First posted on arXiv 14 Sep 2025.

Builds on

11 of 32 references.

References · 11

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  2. 2018Computational Optimal Transportcited by 780
  3. 2018HiDDeN: Hiding Data With Deep Networkscited by 397
  4. 2020Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributionscited by 280
  5. 2019Style Transfer by Relaxed Optimal Transport and Self-Similaritycited by 168
  6. 2017Gradient descent GAN optimization is locally stablecited by 67
  7. 2018ReDMark: Framework for Residual Diffusion Watermarking on Deep Networkscited by 57
  8. 2018Disconnected Manifold Learning for Generative Adversarial Networkscited by 27
  9. 2018BourGAN: Generative Networks with Metric Embeddingscited by 22
  10. 2017Steganographic Generative Adversarial Networkscited by 21
  11. 2023Low-frequency Image Deep Steganography: Manipulate the Frequency Distribution to Hide Secrets with Tenacious Robustnesscited by 3

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Citing papers per year: 2026: 1.

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  1. 2026PDD: Manifold-Prior Diverse Distillation for Medical Anomaly Detectioncited by 0