When the data undergo a distribution change, existing linguistic steganalysis often struggles
to effectively capture the statistical characteristics of the transformed cover or stego, resulting …

Linguistic steganalysis (LS) tasks aim to effectively detect stegos generated by linguistic
steganography. Existing LS methods overlook the distinctive user characteristics, leading to …

Steganography is the technique of hiding secret messages within seemingly harmless
covers to elude examination by censors. Despite having been proposed several decades …

Existing linguistic steganalysis methods require the training and testing datasets to be
independent and identically distributed (iid). However, in real-world scenarios, various types …

In an era where social media platforms burgeon with diverse content, compelling
moderation is imperative to filter harmful materials. Traditional methods often grapple with …

To enhance our ability to model long-range semantical dependencies, we introduce a novel
approach for linguistic steganography through English translation. This method leverages …

Linguistic Steganography (LS) tasks aim to generate steganographic texts (stego) based on
secret information. Only authorized recipients can perceive the existence of secret …

Linguistic steganography is a useful technique to hide secret messages within a normal
cover text, playing a crucial role in the field of data protection. Compared to the data …

The generative linguistic steganography in social networks have potential huge abuse and
regulatory risks, with serious implications for information security, especially in the era of …

Text steganography is a method of covert communication that aims to conceal the existence
of secret information. Steganography has a long history of development and is widely used …