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收稿：2025-06-19，
修回：2025-09-01，
录用：2025-09-11，
纸质出版：2026-01-16
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神经网络模型数量增长迅猛，以神经网络为代表的人工智能技术在很多应用领域取得巨大成功。与此同时，神经网络模型含有大量冗余信息，可为隐藏机密信息提供便利条件，因此可以借助神经网络模型传递机密信息。在此背景下，本文介绍以神经网络为载体的隐写技术。通过与相关技术进行对比，首先概述了神经网络模型隐写的研究意义、基础概念和评价指标；之后依据模型隐写的不同策略，从基于训练的模型隐写、基于修改的模型隐写、基于后门等技术的模型隐写3个不同的角度分别梳理了研究现状，阐述各类方法的核心机制与适用场景，以及分析了各类方法在实际应用中的优缺点。同时也对模型隐写分析的成果进行了分析和讨论，总结白盒和黑盒模型隐写分析技术，揭示当前模型隐写攻防态势。最后对模型隐写技术发展趋势进行了展望，指出大模型隐写、高隐蔽—大容量协同优化、端到端安全传输等未来方向。本文提供了一个关于模型隐写技术的全面视角，旨在展示其在信息安全领域的重要性和潜力。
In recent years， the number of neural network models has increased rapidly， and artificial intelligence technology represented by neural networks has achieved great success in many application fields. Neural network models inherently contain considerable redundant information. This redundancy creates favorable conditions for hiding confidential data. Therefore， neural network models can be used as covers for covert communication. This new paradigm is called neural network model steganography （model steganography）. The steganographer chooses the location where confidential information is embedded in the model and uses a key to embed the confidential information into the model for transmission. The receiver uses the shared key to extract the confidential information in the location where it is embedded. Model steganography is used for covert communication without detection. In recent years， neural network model steganography technology has made great progress. In practice， it can be applied in some scenarios， such as military defense or secret communication between intelligence agencies， embedding confidential information in the model training process or hiding secret tasks in the model. In command distribution， the commander intends to send different commands to multiple officers， or multiple officers send different messages to the commander. Using model steganography allows transferring confidential information without being detected. Meanwhile， by modifying model parameters， malicious developers can embed malicious software into the benign model， resulting in the loss of model users. Using neural network backdoor technology to poison the target model enables performing different tasks defined by the attackers without the users’ knowledge. Technologies related to model steganography include model watermarking and multimedia steganography based on a neural network model. The model watermark takes the neural network as the protection object and embeds the digital watermark in the model to protect the intellectual property rights of the model owner. The watermark information embedded in the model can be extracted correctly without affecting the normal use of the cover model and without deliberately concealing the existence of the watermark information. In addition， the embedding capacity can accommodate the watermark information， so there is no need to pursue large capacity. Multimedia steganography based on a neural network model takes multimedia data as covers and the neural network model as a tool for information embedding and extraction and uses the neural network in each stage of embedding and extraction to embed confidential information in multimedia data. In terms of concealment， model steganography has unique advantages compared with its related technologies. The steganography of the model is naturally hidden. The model itself is a complex set of high-dimensional parameters， so a small number of parameter disturbances in the model are difficult to detect. Model steganography is usually achieved by modifying redundant parameters， which will not affect the function of the model. Regarding embedding capacity， model steganography has the potential of supercapacity compared with its related technologies. Model steganography can use parameter redundancy to embed data， and the neural network has a large number of parameters， so it can embed substantial information even if the minimum proportion parameters are modified. In accordance with the different strategies of model steganography， the existing methods can be divided into three categories： model steganography based on training， modification， and backdoor technology. Most of the results of model steganography are training-based model steganography. The main idea of training-based model steganography is to embed confidential information in the process of the training model. In the hidden layer of the model， the sender first selects the weight used to embed the confidential information and then embeds the confidential information into the model under the key function through the training model. In the output layer， the model output is required to be as similar as the confidential information as possible， and the model weight is constantly updated under the guidance of the confidential information. The basic idea of model steganography based on modification is to modify the model parameters to match the confidential information to achieve the purpose of embedding confidential information. Malicious payloads can be embedded without significantly affecting the model performance by replacing malware bytes or mapping model parameters to hide malware in the model. At the sending end， malicious developers choose to modify the location of model parameters to embed malicious software into the model. At the receiving end， they determine the location where the malicious software is embedded in the model parameters， extract the malicious software， check the integrity， and run the malicious software. Model steganography based on backdoor technology uses backdoor technology. Attackers bury backdoors in the model， making the infected model behave normally in general. However， when the backdoor trigger is activated， the output of the model will become the malicious target set by the attacker in advance. This method poisons the target model and can extract additional information from the output of the model. For the analysis method of model steganalysis， on the basis of whether the steganalyzer needs to master the internal details of the neural network model， current model steganalysis algorithms can be classified as white and black box model steganalysis. White box model steganalysis means that the analyst has knowledge and access rights to the internal structure and parameters of the model to detect and analyze the confidential information hidden in the model. Black box model steganalysis treats the target model as a “black box”， without accessing its internal structure and weight parameter details， to detect and analyze whether the model contains secret. To review the latest developments and trends， this study analyzes advanced methodologies in model steganography as follows： 1） it introduces the purpose and goal of model steganography， as well as its basic concepts， evaluation indicators， and technology classification. 2） The development status of model steganography is summarized and analyzed. 3） The advantages and disadvantages are compared and evaluated. 4） The development trend of model steganography is explored.
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