In this paper, we make the first attempt towards defining cost function of steganography with large language models (LLMs), which is totally different from previous works that rely heavily on expert knowledge or require large-scale datasets for cost learning. To achieve this goal, a two-stage strategy combining LLM-guided program synthesis with evolutionary search is applied in the proposed method. In the first stage, a certain number of cost functions in the form of computer program are synthesized from LLM responses to structured prompts. These cost functions are then evaluated with pretrained steganalysis models so that candidate cost functions suited to steganography can be collected. In the second stage, by retraining a steganalysis model for each candidate cost function, the optimal cost function(s) can be determined according to the detection accuracy. This two-stage strategy is performed by an iterative fashion so that the best cost function can be collected at the last iteration. Experiments show that the proposed method enables LLMs to design new cost functions of steganography that significantly outperform existing works in terms of resisting steganalysis tools, which verifies the superiority of the proposed method. To the best knowledge of the authors, this is the first work applying LLMs to the design of advanced cost function of steganography, which presents a novel perspective for steganography design and may shed light on further research.

翻译：本文首次尝试利用大语言模型（LLMs）定义隐写术的代价函数，这与以往严重依赖专家知识或需要大规模数据集进行代价学习的方法完全不同。为实现这一目标，所提方法采用了一种结合LLM引导的程序合成与进化搜索的两阶段策略。在第一阶段，通过LLM对结构化提示的响应，合成了若干以计算机程序形式表示的代价函数。随后，利用预训练的隐写分析模型对这些代价函数进行评估，从而筛选出适用于隐写术的候选代价函数。在第二阶段，通过为每个候选代价函数重新训练一个隐写分析模型，根据检测准确率确定最优的代价函数。该两阶段策略以迭代方式执行，从而在最后一次迭代中收集到最佳代价函数。实验表明，所提方法使LLMs能够设计出新的隐写术代价函数，其在抵抗隐写分析工具方面显著优于现有工作，验证了该方法的优越性。据作者所知，这是首次将LLMs应用于高级隐写术代价函数设计的研究，为隐写术设计提供了新的视角，并可能启发进一步的研究。