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A Burn After Reading Data Hiding Framework
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AI
DOI：10.1109/LSP.2025.3545805.png)
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Abstract
En 中文
We propose a novel data hiding framework based on a multimodal generative model, named Burn After Reading Data Hiding (BarDH). Unlike previous related work in the field of data hiding, our proposed BarDH introduces a novel function: once the receiver extracts the secret data, the secret data cannot be extracted again. We have named this function as 'Burn After Reading'. The concept of 'Burn After Reading' was first introduced into data hiding research. We believe that this mechanism constitutes a highly effective means of safeguarding secret data, such as in scenarios where the receiver's device has been hacked or stolen. Our proposed BarDH model enhances the multimodal generative model, Latent Diffusion Models (LDMs), better aligning it with data hiding tasks and requirements. Experimental results demonstrate that the proposed BarDH framework effectively facilitates the functionality of 'Burn After Reading'. Simultaneously, the framework demonstrates competitiveness in both the accuracy of secret data extraction and security.
Keywords:
Data mining
Data models
Standards
Gaussian distribution
Servers
Receivers
Diffusion models
Training
Security
Predictive models
Burn after reading
data hiding
LDMs
multimodal generative model
Journal
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