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.

翻译：我们提出了一种基于潜在空间迭代优化的鲁棒且可证明安全的图像隐写框架。在此框架中，接收方将传输图像视为固定参考，并通过迭代优化潜在变量以最小化重构误差，从而提高信息提取的准确性。与现有方法不同，我们的方法在显著增强图像压缩及各种图像处理场景下鲁棒性的同时，保持了嵌入过程的可证明安全性。在基准数据集上的实验结果表明，所提出的迭代优化方法不仅能在保持可证明安全性的同时提升对图像压缩的鲁棒性，还可作为一个独立模块应用于其他可证明安全的隐写方案，以进一步增强其鲁棒性。这凸显了潜在空间优化在构建可靠、鲁棒且安全的隐写系统中的实用性与前景。