A new LLM-based steganography framework encodes secret bits in the choice of entities mentioned in generated text, claiming higher capacity and robustness, but its indistinguishability claim is not supported and is contradicted by its own metrics.
Towards Near-imperceptible Steganographic Text
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abstract
We show that the imperceptibility of several existing linguistic steganographic systems (Fang et al., 2017; Yang et al., 2018) relies on implicit assumptions on statistical behaviors of fluent text. We formally analyze them and empirically evaluate these assumptions. Furthermore, based on these observations, we propose an encoding algorithm called patient-Huffman with improved near-imperceptible guarantees.
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Semantic Steganography: A Framework for Robust and High-Capacity Information Hiding using Large Language Models
A new LLM-based steganography framework encodes secret bits in the choice of entities mentioned in generated text, claiming higher capacity and robustness, but its indistinguishability claim is not supported and is contradicted by its own metrics.