Robust Provably Secure Image Steganography via Latent Iterative Optimization

TL;DR

This paper introduces a new image steganography method that uses latent-space iterative optimization to enhance robustness against image processing while maintaining provable security, demonstrated through experiments on benchmark datasets.

Contribution

It presents a novel latent-space iterative optimization framework that improves robustness in secure image steganography without compromising provable security.

Findings

Enhanced robustness against image compression and processing

Maintains provable security of the embedding

Applicable as an independent module for robustness reinforcement

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.