ข้อมูลเชิงลึก - Computer Security and Privacy - # Generative Text Steganography with Large Language Models

Generative Text Steganography Using Large Language Models for Secure Covert Communication

Q: How can the proposed LLM-Stega be further improved to leverage the full potential of large language models for generative text steganography?

The proposed LLM-Stega can be enhanced in several ways to fully leverage the capabilities of large language models (LLMs) for generative text steganography. Fine-tuning Strategies: Implementing specialized fine-tuning strategies can help optimize the LLMs for steganographic purposes. By fine-tuning the model on steganography-specific datasets, the LLM can learn to generate stego texts with higher quality, security, and embedding capacity. Advanced Prompt Engineering: Further refining the prompts used in the steganographic process can improve the quality and security of the generated stego texts. By designing more sophisticated prompts that guide the LLM to encode and extract secret messages effectively, the overall performance of LLM-Stega can be enhanced. Multi-Model Fusion: Integrating multiple LLMs or different types of language models can potentially improve the diversity and richness of the generated stego texts. By combining the strengths of various models, LLM-Stega can achieve better text quality and security. Adversarial Training: Incorporating adversarial training techniques can enhance the robustness of LLM-Stega against steganalysis attacks. By training the model to withstand various detection methods, the security of the steganographic communication can be strengthened. Dynamic Keyword Selection: Implementing a dynamic keyword selection mechanism based on the context of the cover text can improve the relevance and coherence of the generated stego texts. By adapting the keyword set to the specific content being encoded, LLM-Stega can generate more contextually appropriate stego messages.

Q: How can the proposed techniques be adapted or extended to other domains, such as image or audio steganography, to enable secure covert communication across different media?

The techniques proposed in LLM-Stega for generative text steganography can be adapted and extended to other domains, such as image or audio steganography, to facilitate secure covert communication across different media. Here are some ways to achieve this adaptation: Image Steganography: For image steganography, the keyword set construction and encrypted steganographic mapping concepts from LLM-Stega can be translated into pixel manipulation and encoding schemes. Instead of words, image features or pixel values can be used as the basis for embedding secret information. The optimization mechanism based on reject sampling can be applied to ensure accurate extraction of hidden data. Audio Steganography: In audio steganography, the principles of keyword selection and steganographic mapping can be applied to audio features or signal properties. By mapping secret messages to specific audio characteristics, such as frequency components or time-domain samples, secure communication can be achieved. The optimization mechanism can be tailored to the audio domain to maintain imperceptibility and security. Multi-Media Fusion: To enable communication across different media types, a unified framework can be developed that integrates text, image, and audio steganography techniques. By combining the methodologies for each domain and establishing cross-media encoding strategies, a comprehensive steganographic system can be created for seamless covert communication. Cross-Domain Transfer Learning: Leveraging transfer learning techniques, the knowledge and insights gained from text steganography can be transferred to image and audio steganography domains. By adapting the prompt engineering and optimization mechanisms to different media types, the proposed techniques can be effectively extended to ensure secure communication across diverse media formats.

แนวคิดหลัก

A black-box generative text steganographic method based on the user interfaces of large language models, called LLM-Stega, is proposed to enable secure covert communication between Alice and Bob.

บทคัดย่อ

The paper explores a black-box generative text steganographic method based on the user interfaces of large language models (LLMs), called LLM-Stega. The main goal is to enable secure covert communication between Alice (sender) and Bob (receiver) by using the user interfaces of LLMs.

Key highlights:

Constructs a keyword set and designs a new encrypted steganographic mapping to embed secret messages.
Proposes an optimization mechanism based on reject sampling to guarantee accurate extraction of secret messages and rich semantics of generated stego texts.
Comprehensive experiments demonstrate that the proposed LLM-Stega outperforms current state-of-the-art methods in terms of embedding capacity and security.

The paper first discusses the limitations of existing generative text steganographic methods, which are white-box and require access to the language model and training vocabulary. To address this, the authors propose LLM-Stega, a black-box approach that uses the user interfaces of LLMs to generate stego texts and extract secret messages.

The key components of LLM-Stega are:

Keyword Set Construction: A keyword set is constructed, containing subject, predicate, object, and emotion keywords, to encode secret messages.
Encrypted Steganographic Mapping: An encrypted steganographic mapping is designed to map secret messages into the location indices and repetition numbers of the keywords in the augmented keyword set.
Steganographic Text Generation and Secret Message Extraction: An embedding prompt is used to generate stego texts, and an extraction prompt is used to extract the secret messages. A feedback optimization mechanism based on reject sampling is proposed to ensure accurate extraction and rich semantics of the generated stego texts.

The paper presents comprehensive experiments evaluating the performance of LLM-Stega in terms of text quality, embedding capacity, anti-steganalysis ability, and human evaluation. The results demonstrate the superiority of LLM-Stega over state-of-the-art methods.

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สถิติ

The average length of the News dataset is about 15 words.
The average perplexity of the normal sentence in the News dataset is 185.64.

คำพูด

"Recent advances in large language models (LLMs) have blurred the boundary of high-quality text generation between humans and machines, which is favorable for generative text steganography."
"While, current advanced steganographic mapping is not suitable for LLMs since most users are restricted to accessing only the black-box API or user interface of the LLMs, thereby lacking access to the training vocabulary and its sampling probabilities."

ข้อมูลเชิงลึกที่สำคัญจาก

Generative Text Steganography with Large Language Model

by Jiaxuan Wu,Z... ที่ arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10229.pdf

Generative Text Steganography with Large Language Model

สอบถามเพิ่มเติม

How can the proposed LLM-Stega be further improved to leverage the full potential of large language models for generative text steganography?

The proposed LLM-Stega can be enhanced in several ways to fully leverage the capabilities of large language models (LLMs) for generative text steganography.

Fine-tuning Strategies: Implementing specialized fine-tuning strategies can help optimize the LLMs for steganographic purposes. By fine-tuning the model on steganography-specific datasets, the LLM can learn to generate stego texts with higher quality, security, and embedding capacity.

Advanced Prompt Engineering: Further refining the prompts used in the steganographic process can improve the quality and security of the generated stego texts. By designing more sophisticated prompts that guide the LLM to encode and extract secret messages effectively, the overall performance of LLM-Stega can be enhanced.

Multi-Model Fusion: Integrating multiple LLMs or different types of language models can potentially improve the diversity and richness of the generated stego texts. By combining the strengths of various models, LLM-Stega can achieve better text quality and security.

Adversarial Training: Incorporating adversarial training techniques can enhance the robustness of LLM-Stega against steganalysis attacks. By training the model to withstand various detection methods, the security of the steganographic communication can be strengthened.

Dynamic Keyword Selection: Implementing a dynamic keyword selection mechanism based on the context of the cover text can improve the relevance and coherence of the generated stego texts. By adapting the keyword set to the specific content being encoded, LLM-Stega can generate more contextually appropriate stego messages.

How can the proposed techniques be adapted or extended to other domains, such as image or audio steganography, to enable secure covert communication across different media?

The techniques proposed in LLM-Stega for generative text steganography can be adapted and extended to other domains, such as image or audio steganography, to facilitate secure covert communication across different media. Here are some ways to achieve this adaptation:

Image Steganography: For image steganography, the keyword set construction and encrypted steganographic mapping concepts from LLM-Stega can be translated into pixel manipulation and encoding schemes. Instead of words, image features or pixel values can be used as the basis for embedding secret information. The optimization mechanism based on reject sampling can be applied to ensure accurate extraction of hidden data.

Audio Steganography: In audio steganography, the principles of keyword selection and steganographic mapping can be applied to audio features or signal properties. By mapping secret messages to specific audio characteristics, such as frequency components or time-domain samples, secure communication can be achieved. The optimization mechanism can be tailored to the audio domain to maintain imperceptibility and security.

Multi-Media Fusion: To enable communication across different media types, a unified framework can be developed that integrates text, image, and audio steganography techniques. By combining the methodologies for each domain and establishing cross-media encoding strategies, a comprehensive steganographic system can be created for seamless covert communication.

Cross-Domain Transfer Learning: Leveraging transfer learning techniques, the knowledge and insights gained from text steganography can be transferred to image and audio steganography domains. By adapting the prompt engineering and optimization mechanisms to different media types, the proposed techniques can be effectively extended to ensure secure communication across diverse media formats.