Provably Secure Robust Image Steganography via Cross-Modal Error Correction

TL;DR

This paper introduces a provably secure, high-quality, and robust image steganography method leveraging autoregressive models and cross-modal error correction to improve embedding capacity, robustness against lossy processing, and undetectability.

Contribution

It proposes a novel steganography approach combining state-of-the-art autoregressive image generation with cross-modal error correction for enhanced security and robustness.

Findings

Improved stego image quality and embedding capacity.

Enhanced robustness against JPEG compression and lossy processing.

Maintained provable undetectability of steganographic images.

Abstract

The rapid development of image generation models has facilitated the widespread dissemination of generated images on social networks, creating favorable conditions for provably secure image steganography. However, existing methods face issues such as low quality of generated images and lack of semantic control in the generation process. To leverage provably secure steganography with more effective and high-performance image generation models, and to ensure that stego images can accurately extract secret messages even after being uploaded to social networks and subjected to lossy processing such as JPEG compression, we propose a high-quality, provably secure, and robust image steganography method based on state-of-the-art autoregressive (AR) image generation models using Vector-Quantized (VQ) tokenizers. Additionally, we employ a cross-modal error-correction framework that generates stego text from stego images to aid in restoring lossy images, ultimately enabling the extraction of secret messages embedded within the images. Extensive experiments have demonstrated that the proposed method provides advantages in stego quality, embedding capacity, and robustness, while ensuring provable undetectability.