mlsanityData to 7 Oct 2026

Foveation Improves Payload Capacity in Steganography

arXiv 2510.13151 ↗First posted 15 Oct 2025cs.CV cs.GRcited by 0

Not on the map. Why?

Abstract (arXiv)

Steganography finds its use in visual medium such as providing metadata and watermarking. With support of efficient latent representations and foveated rendering, we trained models that improve existing capacity limits from 100 to 500 bits, while achieving better accuracy of up to 1 failure bit out of 2000, at 200K test bits. Finally, we achieve a comparable visual quality of 31.47 dB PSNR and 0.13 LPIPS, showing the effectiveness of novel perceptual design in creating multi-modal latent representations in steganography.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 7 references that are in the map. First posted on arXiv 15 Oct 2025.

Builds on

4 of 7 references.

References · 4

  1. 2020Training Generative Adversarial Networks with Limited Datacited by 1,184
  2. 2024LaWa: Using Latent Space for In-Generation Image Watermarkingcited by 27
  3. 2021Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganographycited by 23
  4. 2024Learned Single-Pass Multitasking Perceptual Graphics for Immersive Displayscited by 1

Built on by

Not cited yet.