通过隐空间迭代优化，实现鲁棒且可证明安全的图像隐写。
Robust Provably Secure Image Steganography via Latent Iterative Optimization
- 接收方在隐空间迭代优化隐变量以提升解码准确率。
- 在多种压缩和图像处理下仍保持高鲁棒性与安全保证。
- 可作为通用模块增强其他安全隐写方案的抗干扰能力。
我们提出一种基于隐空间迭代优化的鲁棒且可证明安全的图像隐写框架。接收方将传输图像视为固定参考，迭代优化隐变量以最小化重建误差，从而提高信息提取精度。与以往方法不同，该方法在显著提升对各类图像压缩及处理操作的鲁棒性的同时，仍保持嵌入过程的可证明安全性。在基准数据集上的实验表明，所提出的迭代优化不仅增强了对图像压缩的鲁棒性，且可作为独立模块应用于其他可证明安全的隐写方案中，进一步提升其鲁棒性。这凸显了隐空间优化在构建可靠、鲁棒且安全隐写系统方面的实用性与前景。
原文摘要 · Abstract (English)
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.
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