Abstract
Linguistic steganalysis can indicate the existence of steganographic content in suspicious text carriers. Precise linguistic steganalysis on suspicious carrier is critical for multimedia security. In this paper, we introduced a neural linguistic steganalysis approach based on multi-head self-attention. In the proposed steganalysis approach, words in text are firstly mapped into semantic space with a hidden representation for better modeling the semantic features. Then, we utilize multi-head self-attention to model the interactions between words in carrier. Finally, a softmax layer is utilized to categorize the input text as cover or stego. Extensive experiments validate the effectiveness of our approach.
Highlights
Steganography is an ancient technique aiming at embedding secret messages into carriers which can be divided into image steganography [1], text steganography [2], and audio steganography [3] according to the different types of carriers
We can conclude that compared to other linguistic steganalysis methods, the proposed model has achieved the best detection performance on various metrics, including different text formats and different embedding rates
Precise linguistic steganalysis on suspicious carrier is critical for multimedia security
Summary
Steganography is an ancient technique aiming at embedding secret messages into carriers which can be divided into image steganography [1], text steganography [2], and audio steganography [3] according to the different types of carriers. Text steganography is the process of embedding secret data through a cover text so that the existence of the data is invisible/undetectable for adversaries or casual viewers It has been widely considered as an attractive technology to improve the use of conventional cryptography algorithms in the area of multimedia security by concealing a secret message/watermark into a cover text file/message to protect confidential information. This technology can be used by terrorists and other criminals for malicious purposes, which poses great threats to security in cyberspace. Text steganography technology has been significantly changed with the significant development of natural language processing technology. us, it is crucial to propose a linguistic steganalysis approach with most recent technologies
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