Neural Linguistic Steganography (1909.01496v1)

Abstract: Whereas traditional cryptography encrypts a secret message into an unintelligible form, steganography conceals that communication is taking place by encoding a secret message into a cover signal. Language is a particularly pragmatic cover signal due to its benign occurrence and independence from any one medium. Traditionally, linguistic steganography systems encode secret messages in existing text via synonym substitution or word order rearrangements. Advances in neural LLMs enable previously impractical generation-based techniques. We propose a steganography technique based on arithmetic coding with large-scale neural LLMs. We find that our approach can generate realistic looking cover sentences as evaluated by humans, while at the same time preserving security by matching the cover message distribution with the LLM distribution.

Overview of Neural Linguistic Steganography

The paper "Neural Linguistic Steganography" presents a novel approach to linguistic steganography leveraging advances in neural LLMs, particularly examining the use of arithmetic coding. Steganography, distinct from cryptography, aims to hide the presence of communication itself, rather than merely concealing the content. This research capitalizes on the capabilities of large-scale neural models to efficiently encode messages as natural language text that is indistinguishable from typical input to human and machine observers.

Key Contributions

The paper makes significant contributions in two primary areas. First, it introduces a steganography method that combines arithmetic coding with large neural LLMs, achieving nearly optimal statistical security in theory. Second, extensive human evaluations demonstrate that the generated cover text from this method convincingly mimics typical language, thus fooling human observers even within contextual setups.

Theoretical Foundations and Methodology

The proposed steganography system relies on arithmetic coding—a data compression technique optimal for encoding probability distributions—in conjunction with GPT-2, one of the state-of-the-art neural LLMs. The paper explores a scenario where secret messages are mapped to natural language sequences, aiming to minimize the KL divergence between the generated distribution and true language distribution, thereby enhancing security against statistical steganalysis.

In practice, the method reverses traditional arithmetic coding use, starting the process with the secret message and then generating text that seems innocuous. This setup theoretically achieves a zero KL divergence, ensuring that the cover distribution matches the LLM's distribution over long sequences.

Results and Experimental Insights

The methodology was evaluated via a series of experiments utilizing a variety of steganography approaches, pitting the proposed arithmetic coding strategy against existing block and Huffman encoding methods within the same neural framework. Notable findings include:

• Efficiency in Encoding: The arithmetic coding method demonstrated the ability to achieve low KL divergence while efficiently encoding bits per word, outperforming baseline methods.
• Human and Statistical Evaluation: At around 1 bit per word, the cover text produced by this method was indistinguishable from human-written text as judged by human evaluators. This aligns with the theoretical underpinnings of the approach but also highlights the practical limits imposed by current LLMs' capacities.

Implications and Future Perspectives

This research showcases the potential for neural LLMs to significantly enhance the effectiveness of steganographic systems. The implications could span various domains requiring covert communication, including secure digital interactions where inconspicuousness is paramount. The theoretical and practical insights offered in this paper suggest a firm basis for further exploration into the integration of advanced LLMs in steganography.

Given the rapid development in LLM performance, future iterations of this methodology can anticipate more robust implementations, leveraging the exponential advancements in model fluency and comprehension capacity. Future work may focus on optimizing neural steganography for broader applications and refining the models to ensure higher-quality generations at lower compression ratios, thereby achieving ideal conditions for fooling human and machine surveillance.

In summary, this paper is a substantive step in neural linguistic steganography, warranting further investigation and application in the evolving landscape of AI-driven communication systems.