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.
Abstract (translated)
我们提出了一种基于潜在空间迭代优化的稳健且可证明安全的图像隐写框架。在此框架下，接收方将传输的图像视为固定参考，并通过迭代改进一个潜在变量来最小化重构误差，从而提高消息提取的准确性。与以往的方法不同，我们的方法在保持嵌入可证明安全性的同时，在各种压缩和图像处理场景中显著增强了鲁棒性。 在基准数据集上的实验结果表明，所提出的迭代优化不仅能够在保证可证明安全性的前提下增强对图像压缩的抗干扰能力，还可以作为一个独立模块应用于其他可证明安全的隐写方案中以进一步增强其鲁棒性。这突显了潜在空间优化在构建可靠、稳健且安全的隐写系统中的实用性和前景。
URL
https://arxiv.org/abs/2603.09348
