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基于BN优化SNGAN的自适应音频隐写
引用本文:岳峰,朱慧,苏兆品,张国富.基于BN优化SNGAN的自适应音频隐写[J].计算机学报,2022,45(2):427-440.
作者姓名:岳峰  朱慧  苏兆品  张国富
作者单位:合肥工业大学计算机与信息学院 合肥 230601;工业安全与应急技术安徽省重点实验室(合肥工业大学) 合肥230601,合肥工业大学计算机与信息学院 合肥 230601,合肥工业大学计算机与信息学院 合肥 230601;大数据知识工程教育部重点实验室(合肥工业大学) 合肥230601;智能互联系统安徽省实验室(合肥工业大学) 合肥230009;工业安全与应急技术安徽省重点实验室(合肥工业大学) 合肥230601
基金项目:安徽省重点研究与开发计划(202004d07020011,202104d07020001);;教育部人文社会科学研究青年基金项目(19YJC870021);;中央高校基本科研业务费专项资金项目(PA2020GDKC0015,PA2021GDSK0073,PA2021GDSK0074)资助~~;
摘    要:音频隐写术是将秘密信息(如文本、图像、音频、视频等)隐藏到载体音频中,不仅能够保证秘密信息本身的安全,而且能保证秘密信息传输的安全,已成为信息隐藏领域的研究热点之一.近年来,基于深度学习的音频隐写分析技术能够在充分挖掘隐写深度特征的基础上实现高效的隐写检测,导致隐写术的安全性降低,为隐写术带来了新的挑战.不过,生成对抗...

关 键 词:音频隐写  生成对抗网络  频谱归一化  批处理归一化  自适应隐写

An Adaptive Audio Steganography Using BN Optimizing SNGAN
YUE Feng,ZHU Hui,SU Zhao-Pin,ZHANG Guo-Fu.An Adaptive Audio Steganography Using BN Optimizing SNGAN[J].Chinese Journal of Computers,2022,45(2):427-440.
Authors:YUE Feng  ZHU Hui  SU Zhao-Pin  ZHANG Guo-Fu
Affiliation:(School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230601;Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology),Ministry of Education,Hefei 230601;Intelligent Interconnected Systems Laboratory of Anhui Province(Hefei University of Technology),Hefei 230009;Anhui Province Key Laboratory of Industry Safety and Emergency Technology(Hefei University of Technology),Hefei 230601)
Abstract:Audio steganography,which aims at hiding the secret information(i.e.,text,image,audio,video,etc)into the audio carrier to not only ensure the security of the secret information itself,but also guarantee the transmission security,has become one of the hot topics in the field of information hiding.In recent years,the deep learning based audio steganalysis exhibits high detection efficiency by fully mining the deep features of steganography,which reduces the steganography security and brings new challenges to steganography.Fortunately,the rapid development of generative adversarial networks(GAN)provides a new solution to audio steganography.However,the existing GAN based audio steganography approaches cannot achieve a balance between hiding capacity,imperceptibility,and anti-detection ability,which is difficult to meet the needs of actual applications.In this work,an adaptive audio steganography approach,named BNSNGAN,is proposed on the basis of the optimized spectral normalization GAN by combining batch normalization(BN)with spectrum normalization(SN)in the network structure unit.Specifically,a steganographic encoder is first designed,in which the zero-padding in the time domain is used to preprocesses the secret audio to embed the secret audio with arbitrary length,thus improving the imperceptibility.Secondly,a steganographic extractor with a parallel structure is designed,in which different convolution cores are adopted for deconvolution and improving the accuracy of secret information extraction.Then,a steganalyzer with a cross entropy based loss function is designed to improve the anti-detection ability of audio steganography.Finally,comparative experiments demonstrate that according to the mutual learning of encoding,extractor,and steganography analyzer,the proposed BNSNGAN is able to embed the secret audio with arbitrary length,has a high rate of secret information extraction,and achieves a good balance between hiding capacity,imperceptibility,and anti-detection ability.
Keywords:audio steganography  generative adversarial networks  spectral normalization  batch normalization  adaptive steganography
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