BARStega: A Burn After Reading Steganography Model Based on Latent Diffusion Model.

Abstract

We present 'Burn After Reading' - an innovative data protection mechanism featuring self-destructive capability - and integrate it into the steganography model, thus establishing the Burn After Reading Steganography Model (BARStega), a new architectural paradigm for secret data steganography. The concept of 'Burn After Reading' was first introduced into steganography research, it can be characterized as a self-destructive steganography protocol in which the stego image becomes invalidated after the initial data extraction operation, thus preventing subsequent attempts at secret data extraction. Our BARStega model ensures persistent protection of preexisting secret data, even in scenarios where the receiver's model is stolen by adversary. Through systematic optimization of the Stable Diffusion architecture for steganography requirements, our BARStega model enables both high controllability and high visual quality in stego image generation. Experimental results demonstrate that the proposed BARStega effectively facilitates the functionality of 'Burn After Reading'. Moreover, the model demonstrates competitiveness in both the accuracy of secret data extraction and security.