Journal
IEEE SIGNAL PROCESSING LETTERS
Volume 28, Issue -, Pages 409-413Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2021.3058889
Keywords
Semantics; Data mining; Decoding; Security; Linguistics; Feature extraction; Encoding; Linguistic steganography; sentence generation; cognitive-Imperceptibility; semantic controllable
Categories
Funding
- National Natural Science Foundation of China [61972057, 62002197, U1936208, 61862002]
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Linguistic generative steganography has seen significant developments in recent years, with an emphasis on optimizing the imperceptibility of generated steganographic text. However, a new challenge of cognitive-imperceptibility has been identified, highlighting the need to control semantic expression in order to mitigate potential security risks. Preliminary attempts have shown promising results in enhancing the cognitive-imperceptibility of generated steganographic text.
In recent years, linguistic generative steganography has been greatly developed. The previous works are mainly to optimize the perceptual-imperceptibility and statistical-imperceptibility of the generated steganographic text, and the latest developments show that they have been able to generate steganographic texts that look authentic enough. However, we noticed that these works generally cannot control the semantic expression of the generated steganographic text, and we believe this will bring potential security risks. We named this kind of security challenges as cognitive-imperceptibility. We think this is a new challenge that the generative steganography models must strive to overcome in the future. In this letter, we conduct some preliminary attempts to solve this challenge. Experimental results show that the proposed methods can further constrain the semantic expression of the generated steganographic text on the basis of ensuring certain perceptual-imperceptibility and statistical-imperceptibility, so as to enhance its cognitive-imperceptibility.
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