基于合成语音的计算安全隐写方法

李梦涵; 陈可江; 张卫明; 俞能海

doi:10.11959/j.issn.2096-109x.2022025

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基于合成语音的计算安全隐写方法

学术论文 | 更新时间：2024-06-03

- 基于合成语音的计算安全隐写方法
- Computationally secure steganography based on speech synthesis
- 网络与信息安全学报 2022年8卷第3期页码：134-141
- 作者机构：
  
  1. 中国科学技术大学网络空间安全学院，安徽合肥 230001
  2. 中国科学院电磁空间信息重点实验室，安徽合肥 230001
- 作者简介：
  
  [ "李梦涵（1998− ），女，河北保定人，中国科学技术大学硕士生，主要研究方向为信息隐藏" ]
  [ "陈可江（1994− ），男，浙江温州人，博士（后），主要研究方向为信息隐藏、人工智能安全" ]
  [ "张卫明（1976− ），男，河北定州人，中国科学技术大学教授、博士生导师，主要研究方向为信息隐藏、多媒体内容安全、人工智能安全" ]
  [ "俞能海（1964− ），男，安徽无为人，中国科学技术大学教授、博士生导师，主要研究方向为多媒体信息检索、图像处理与视频通信、数字媒体内容安全" ]
- 基金信息：
  
  国家自然科学基金;The National Natural Science Foundation of China(62102386);国家自然科学基金;The National Natural Science Foundation of China(62002334);国家自然科学基金;The National Natural Science Foundation of China(62072421);国家自然科学基金;The National Natural Science Foundation of China(62121002);中国博士后基金面上项目;China Postdoctoral Science Foundation(2021M693091);安徽省自然科学基金;The Nature Science Foundation of Anhui Province(2008085QF296)
- DOI：10.11959/j.issn.2096-109x.2022025
  中图分类号： TP309
- 网络首发：2022-06，
  
  纸质出版：2022-06-15
- 稿件说明：
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李梦涵, 陈可江, 张卫明, 等. 基于合成语音的计算安全隐写方法[J]. 网络与信息安全学报, 2022,8(3):134-141.

Menghan LI, Kejiang CHEN, Weiming ZHANG, et al. Computationally secure steganography based on speech synthesis[J]. Chinese Journal of Network and Information Security, 2022, 8(3): 134-141.
李梦涵, 陈可江, 张卫明, 等. 基于合成语音的计算安全隐写方法[J]. 网络与信息安全学报, 2022,8(3):134-141. DOI： 10.11959/j.issn.2096-109x.2022025.

Menghan LI, Kejiang CHEN, Weiming ZHANG, et al. Computationally secure steganography based on speech synthesis[J]. Chinese Journal of Network and Information Security, 2022, 8(3): 134-141. DOI： 10.11959/j.issn.2096-109x.2022025.

摘要

计算安全的隐写理论很早就被提出，但一直不能用于主流的以多媒体数据为载体的隐写术。原因在于计算安全隐写的前提是可以获得载体的精确分布或可以按照载体分布精确采样，而自然采集的图像、音/视频无法满足这个前提条件。近几年，随着深度学习的发展，多媒体生成技术逐渐成熟且在互联网上的应用越来越普遍，生成媒体成为合理的隐写载体，隐写者可以用正常的生成媒体掩盖秘密通信，即在媒体生成过程中隐写信息，并与正常的生成媒体不可区分。一些生成模型学到的分布是可知或可控的，这将为计算安全隐写推向实用提供契机。以当前广泛应用的合成语音模型为例，设计并实现了计算安全的对称密钥隐写算法，即在音频生成过程中，根据样本点的条件概率，按算术编码的译码过程将消息解压缩到合成音频中，消息接收方拥有相同的生成模型，通过复现音频合成过程完成消息提取。在该算法的基础上进一步设计了公钥隐写算法，为实现包括隐蔽密钥交换在内的全流程隐蔽通信提供了算法支撑，在保证隐写内容安全的同时，还可以实现隐写行为安全。理论分析显示，所提隐写算法的安全性由嵌入消息的随机性决定，隐写分析实验进一步验证了当前技术下攻击者无法区分合成的载体音频与载密音频。

Abstract

The steganography theory of computing security has been proposed for a long time

but it has not been widely adopted for mainstream steganography using multimedia data as a carrier.The reason is that the prerequisite for calculating secure steganography is to obtain the accurate distribution of the carrier or to accurately sample according to the carrier distribution.However

naturally collected images and audio/video cannot meet this prerequisite.With the development of deep learning technology

various machine-generated media such as image generation and synthesized speech

have become more and more common on the Internet and then generated media has become a reasonable steganography carrier.Steganography can use normal generated media to cover up secret communications

and pursue in distinguishability from normal generated media.The distribution learned by some generative models is known or controllable

which provides an opportunity to push computational security steganography for practical use.Taking the widely used synthetic speech model as an example

a computationally secure symmetric key steganography algorithm was designed and implemented.The message was decompressed into the synthetic audio according to the decoding process of arithmetic coding based on the conditional probability of sample points

and the message receiver had the same generation model to complete the message extraction by reproducing the audio synthesis process.The public key steganography algorithm was additionally designed based on this algorithm

which provided algorithmic support for the realization of full-flow steganographic communication.Steganographic key exchange ensured the security of steganographic content and the security of steganographic behavior was also achieved.The theoretical analysis showed that the security of the proposed algorithm is determined by the randomness of the embedded message.And the steganography analysis experiment further verified that the attacker cannot distinguish the synthesized carrier audio from the encrypted audio.

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