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In this paper, a novel linguistic steganography with high imperceptibility and undetectability is proposed via secret message compression and candidate text selection. The length of the practical embedded payload can be reduced by the proposed word indexing compression algorithm(WIC), while a best stego text with high undetectability can be selected from candidates by the stego text selection strategy. WIC algorithm losslessly compresses the secret message by combining a minimum maximum weight algorithm with Huffman coding under the help of the candidate cover text. To improve the anti-steganalysis capability, ten cover texts with small compression ratios are selected from a huge cover text set, and are embedded the corresponding compressed secret message by using synonym substitutions. Only one stego text is selected by a given rule derived from the distance between a cover text and its stego text. Experimental results show that the proposed compression algorithm achieves better compression ratios than Huffman and LZW coding algorithms leading to higher embedding efficiency, and our steganography performs well in anti-steganalysis capability with compression and the stego text selection rule. [ABSTRACT FROM AUTHOR]

A linguistic steganalysis method is proposed to detect synonym substitution-based steganography, which embeds secret message into a text by substituting words with their synonyms. First, attribute pair of a synonym is introduced to represent its position in an ordered synonym set sorting in descending frequency order and the number of its synonyms. As a result of synonym substitutions, the number of high frequency attribute pairs may be reduced while the number of low frequency attribute pairs would be increased. By theoretically analyzing the changes of the statistical characteristics of attribute pairs caused by SS steganography, a feature vector based on the difference of the relative frequencies of different attribute pairs is utilized to detect the secret message. Finally, the impact on the extracted feature vector caused by synonym coding strategies is analyzed. Experimental results demonstrate that the proposed linguistic steganalysis method can achieve better detection performance than previous methods. [ABSTRACT FROM AUTHOR]

With the development of natural language processing, linguistic steganography has become a research hotspot in the field of information security. However, most existing linguistic steganographic methods may suffer from the low embedding capacity problem. Therefore, this paper proposes a character-level linguistic steganographic method (CLLS) to embed the secret information into characters instead of words by employing a long short-term memory (LSTM) based language model. First, the proposed method utilizes the LSTM model and large-scale corpus to construct and train a character-level text generation model. Through training, the best evaluated model is obtained as the prediction model of generating stego text. Then, we use the secret information as the control information to select the right character from predictions of the trained character-level text generation model. Thus, the secret information is hidden in the generated text as the predicted characters having different prediction probability values can be encoded into different secret bit values. For the same secret information, the generated stego texts vary with the starting strings of the text generation model, so we design a selection strategy to find the highest quality stego text from a number of candidate stego texts as the final stego text by changing the starting strings. The experimental results demonstrate that compared with other similar methods, the proposed method has the fastest running speed and highest embedding capacity. Moreover, extensive experiments are conducted to verify the effect of the number of candidate stego texts on the quality of the final stego text. The experimental results show that the quality of the final stego text increases with the number of candidate stego texts increasing, but the growth rate of the quality will slow down. [ABSTRACT FROM AUTHOR]

It is one of the potential threats to the Internet of Things to reveal confidential messages by color image steganography. The existing color image steganalysis algorithm based on channel geometric transformation measures owns higher accuracy than the others, but it fails to utilize the correlation between the gradient amplitudes of different color channels. Therefore, this article points out that the color image steganography weakens the correlation between the gradient amplitudes of different color channels and proposes a color image steganalysis algorithm based on channel gradient correlation. The proposed algorithm extracts the co-occurrence matrix feature from the gradient amplitude residuals among different color channels and then combines it with the existing color image steganalysis features to train the ensemble classifier for color image steganalysis. The experimental results show that, for WOW and S-UNIWARD steganography, compared with the existing algorithms, the proposed algorithm outperforms the existing algorithms. [ABSTRACT FROM AUTHOR]