Provably Robust and Secure Steganography in Asymmetric Resource Scenario

TL;DR

This paper introduces a new provably secure steganography framework designed for asymmetric resource scenarios, enabling secure communication where only the encoder has high resources and the decoder relies solely on the carrier.

Contribution

It proposes a novel steganography method that relaxes hardware requirements and enhances robustness by using permutation-based hiding and a sampling-based decoding approach.

Findings

Framework is effective for generative models.

Demonstrates robustness over noisy channels.

Decouples decoder from model input and architecture.

Abstract

To circumvent the unbridled and ever-encroaching surveillance and censorship in cyberspace, steganography has garnered attention for its ability to hide private information in innocent-looking carriers. Current provably secure steganography approaches require a pair of encoder and decoder to hide and extract private messages, both of which must run the same model with the same input to obtain identical distributions. These requirements pose significant challenges to the practical implementation of steganography, including limited access to powerful hardware and the intolerance of any changes to the shared input. To relax the limitation of hardware and solve the challenge of vulnerable shared input, a novel and practically significant scenario with asymmetric resource should be considered, where only the encoder is high-resource and accessible to powerful models while the decoder can only read the steganographic carriers without any other model's input. This paper proposes a novel provably robust and secure steganography framework for the asymmetric resource setting. Specifically, the encoder uses various permutations of distribution to hide secret bits, while the decoder relies on a sampling function to extract the hidden bits by guessing the permutation used. Further, the sampling function only takes the steganographic carrier as input, which makes the decoder independent of model's input and model itself. A comprehensive assessment of applying our framework to generative models substantiates its effectiveness. Our implementation demonstrates robustness when transmitting over binary symmetric channels with errors.