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Robust Provably Secure Image Steganography via Latent Iterative Optimization

Yanan Li, Zixuan Wang, Qiyang Xiao, Yanzhen Ren

TL;DR

The experimental results demonstrate that the proposed iterative optimization not only improves robustness against image compression while preserving provable security, but can also be applied as an independent module to further reinforce robustness in other provably secure steganographic schemes.

Abstract

We propose a robust and provably secure image steganography framework based on latent-space iterative optimization. Within this framework, the receiver treats the transmitted image as a fixed reference and iteratively refines a latent variable to minimize the reconstruction error, thereby improving message extraction accuracy. Unlike prior methods, our approach preserves the provable security of the embedding while markedly enhancing robustness under various compression and image processing scenarios. On benchmark datasets, the experimental results demonstrate that the proposed iterative optimization not only improves robustness against image compression while preserving provable security, but can also be applied as an independent module to further reinforce robustness in other provably secure steganographic schemes. This highlights the practicality and promise of latent-space optimization for building reliable, robust, and secure steganographic systems.

Robust Provably Secure Image Steganography via Latent Iterative Optimization

TL;DR

The experimental results demonstrate that the proposed iterative optimization not only improves robustness against image compression while preserving provable security, but can also be applied as an independent module to further reinforce robustness in other provably secure steganographic schemes.

Abstract

We propose a robust and provably secure image steganography framework based on latent-space iterative optimization. Within this framework, the receiver treats the transmitted image as a fixed reference and iteratively refines a latent variable to minimize the reconstruction error, thereby improving message extraction accuracy. Unlike prior methods, our approach preserves the provable security of the embedding while markedly enhancing robustness under various compression and image processing scenarios. On benchmark datasets, the experimental results demonstrate that the proposed iterative optimization not only improves robustness against image compression while preserving provable security, but can also be applied as an independent module to further reinforce robustness in other provably secure steganographic schemes. This highlights the practicality and promise of latent-space optimization for building reliable, robust, and secure steganographic systems.
Paper Structure (12 sections, 14 equations, 4 figures, 1 table)

This paper contains 12 sections, 14 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Proposed Method
  3. Embedding Procedure
  4. Extraction Procedure
  5. Latent Optimization
  6. Provable Security Analysis under Latent-Space Optimization
  7. Experiments
  8. Setup
  9. Performance
  10. Ablation
  11. Cross-Model Applicability of Latent-Space Optimization
  12. Conclusion

Figures (4)

  • Figure 1: Overall framework of robust and provably secure image steganography via latent-space iterative optimization.
  • Figure 2: Illustration of latent-space optimization and the trajectory of latent variables.
  • Figure 3: Mean extraction accuracy gain (%) for six image formats at 50/80/100/110 latent-space optimization steps.
  • Figure 4: Generality of latent-space iterative optimization: Hu accuracy across formats (baseline vs. 100 steps).