Robust Provably Secure Image Steganography via Latent Iterative Optimization

2026-03-10

AI Summary

This work addresses the insufficient robustness of image steganography under compression and common image processing operations by proposing a provably secure steganographic framework based on iterative latent-space optimization. At the receiver side, the method leverages the transmitted image as a reference and iteratively optimizes latent variables to minimize reconstruction error, thereby enhancing message extraction accuracy. To the best of our knowledge, this is the first integration of latent-space iterative optimization into a provably secure steganographic system, achieving significantly improved practical robustness without compromising theoretical security guarantees. Experimental results demonstrate that the proposed framework achieves high robustness and security under JPEG compression and similar distortions, and can serve as a plug-and-play module to effectively boost the performance of existing secure steganographic schemes.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Learning to Search

Application Category

• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• image steganography
• provable security
• robustness
• image compression
• latent-space optimization

Innovation

• latent-space optimization
• provably secure steganography
• iterative refinement
• robustness
• image compression