Topic-aware Neural Linguistic Steganography Based on Knowledge Graphs

The core challenge of steganography is always how to improve the hidden capacity and the concealment. Most current generation-based linguistic steganography methods only consider the probability distribution between text characters, and the emotion and topic of the generated steganographic text are uncontrollable. Especially for long texts, generating several sentences related to a topic and displaying overall coherence and discourse-relatedness can ensure better concealment. In this article, we address the problem of generating coherent multi-sentence texts for better concealment, and a topic-aware neural linguistic steganography method that can generate a steganographic paragraph with a specific topic is present. We achieve a topic-controllable steganographic long text generation by encoding the related entities and their relationships from Knowledge Graphs. Experimental results illustrate that the proposed method can guarantee both the quality of the generated steganographic text and its relevance to a specific topic. The proposed model can be widely used in covert communication, privacy protection, and many other areas of information security.

Novel Linguistic Steganography Based on Character-Level Text Generation

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.

IMPLEMENTATION OF STEGANOGRAPHIC INFORMATION PROTECTION METHOD IN SOFTWARE DEVELOPMENT

Показано, что защита программных продуктов от несанкционированного использования является первостепенной задачей для разработчиков. Выделены три основные категории атак на программное обеспечение (ПО): нелегальное использование, обратная разработка, когда из ПО извлекаются наиболее ценные модули или части исходного кода, и модификация исходного кода, приводящая к существенным потерям для разработчиков, вплоть до потери авторских прав. Рассмотрен один из возможных способов защиты метод вложения цифрового водяного знака (ЦВЗ), основанный на использовании лингвистической стеганографии. It is shown that the protection of software products from unauthorized use is a priority for developers. There are three main categories of attacks on modern software: illegal use, reverse development, when the most valuable modules or parts of the source code are extracted from the software, and modification of the source code, which leads to significant loss of developers, up to the loss of copyright. One of the possible methods of protection is the method of embedding a digital watermark, based on the use of linguistic steganography.