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This paper introduces a novel image steganography framework that leverages latent-space iterative optimization to enhance robustness against compression and image processing attacks. The receiver iteratively refines a latent variable to minimize reconstruction error, improving message extraction accuracy while maintaining provable security. Experiments on benchmark datasets demonstrate that this iterative optimization improves robustness and can be integrated into other steganographic schemes.
Provably secure steganography can now withstand real-world image compression and processing thanks to a clever latent-space optimization technique.
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.
