网络语音电话(voice over IP,VoIP)已成为目前人们沟通交流的普遍选择.相比文本、图像等传统隐写载体,网络语音数据流隐蔽性好,隐藏空间更大,因而得到越来越多的关注.目前的网络语音隐写研究围绕算法设计展开,已有成果的抗检测性有待进...网络语音电话(voice over IP,VoIP)已成为目前人们沟通交流的普遍选择.相比文本、图像等传统隐写载体,网络语音数据流隐蔽性好,隐藏空间更大,因而得到越来越多的关注.目前的网络语音隐写研究围绕算法设计展开,已有成果的抗检测性有待进一步加强,且缺乏安全性理论指导.为此,首先分析语音帧的时序性特点,以相对熵的形式定义了基于贝叶斯网络模型的隐写安全性.通过分析语音编码过程,建立了固定码本参数的贝叶斯网络模型并将参数分为二元载体和三元载体2类.应用矩阵嵌入技术确定载体的修改位置,减少修改量;以最小化安全性测度为目标确定三元载体元素的修改方向,降低隐写对载体统计特性的影响.实验表明:在不显著增加计算复杂度的前提下,所提算法取得了比已有算法更好的感知透明性和抗检测能力.展开更多
VoIP(voice over IP)是基于UDP/IP协议族的语音通信技术,当信道环境变差时不可避免地会产生网络分组丢失,这给建立在其上的VoIP隐写的可靠传输带来了挑战。提出利用纠删码对秘密信息进行冗余预处理,再结合矩阵嵌入编码实现最小失真的隐...VoIP(voice over IP)是基于UDP/IP协议族的语音通信技术,当信道环境变差时不可避免地会产生网络分组丢失,这给建立在其上的VoIP隐写的可靠传输带来了挑战。提出利用纠删码对秘密信息进行冗余预处理,再结合矩阵嵌入编码实现最小失真的隐写,从而建立了基于联合编码的嵌入和提取模型。在此基础上,分析了关键参数对联合编码性能的影响并给出了最优参数的选取算法。实验结果表明,所提联合编码能够有效提高隐写系统的抗分组丢失能力,且能减少对语音流的修改。展开更多
Most existing network representation learning algorithms focus on network structures for learning.However,network structure is only one kind of view and feature for various networks,and it cannot fully reflect all cha...Most existing network representation learning algorithms focus on network structures for learning.However,network structure is only one kind of view and feature for various networks,and it cannot fully reflect all characteristics of networks.In fact,network vertices usually contain rich text information,which can be well utilized to learn text-enhanced network representations.Meanwhile,Matrix-Forest Index(MFI)has shown its high effectiveness and stability in link prediction tasks compared with other algorithms of link prediction.Both MFI and Inductive Matrix Completion(IMC)are not well applied with algorithmic frameworks of typical representation learning methods.Therefore,we proposed a novel semi-supervised algorithm,tri-party deep network representation learning using inductive matrix completion(TDNR).Based on inductive matrix completion algorithm,TDNR incorporates text features,the link certainty degrees of existing edges and the future link probabilities of non-existing edges into network representations.The experimental results demonstrated that TFNR outperforms other baselines on three real-world datasets.The visualizations of TDNR show that proposed algorithm is more discriminative than other unsupervised approaches.展开更多
In order to optionally regulate embedding capacity and embedding transparency according to user's requirements in voice-over-IP(VoIP) steganography,a dynamic matrix encoding strategy(DMES) was presented.Differing ...In order to optionally regulate embedding capacity and embedding transparency according to user's requirements in voice-over-IP(VoIP) steganography,a dynamic matrix encoding strategy(DMES) was presented.Differing from the traditional matrix encoding strategy,DMES dynamically chose the size of each message group in a given set of adoptable message sizes.The appearance possibilities of all adoptable sizes were set in accordance with the desired embedding performance(embedding rate or bit-change rate).Accordingly,a searching algorithm that could provide an optimal combination of appearance possibilities was proposed.Furthermore,the roulette wheel algorithm was employed to determine the size of each message group according to the optimal combination of appearance possibilities.The effectiveness of DMES was evaluated in StegVoIP,which is a typical covert communication system based on VoIP.The experimental results demonstrate that DMES can adjust embedding capacity and embedding transparency effectively and flexibly,and achieve the desired embedding performance in any case.For the desired embedding rate,the average errors are not more than 0.000 8,and the standard deviations are not more than 0.002 0;for the desired bit-change rate,the average errors are not more than 0.001 4,and the standard deviations are not more than 0.002 6.展开更多
文摘网络语音电话(voice over IP,VoIP)已成为目前人们沟通交流的普遍选择.相比文本、图像等传统隐写载体,网络语音数据流隐蔽性好,隐藏空间更大,因而得到越来越多的关注.目前的网络语音隐写研究围绕算法设计展开,已有成果的抗检测性有待进一步加强,且缺乏安全性理论指导.为此,首先分析语音帧的时序性特点,以相对熵的形式定义了基于贝叶斯网络模型的隐写安全性.通过分析语音编码过程,建立了固定码本参数的贝叶斯网络模型并将参数分为二元载体和三元载体2类.应用矩阵嵌入技术确定载体的修改位置,减少修改量;以最小化安全性测度为目标确定三元载体元素的修改方向,降低隐写对载体统计特性的影响.实验表明:在不显著增加计算复杂度的前提下,所提算法取得了比已有算法更好的感知透明性和抗检测能力.
文摘VoIP(voice over IP)是基于UDP/IP协议族的语音通信技术,当信道环境变差时不可避免地会产生网络分组丢失,这给建立在其上的VoIP隐写的可靠传输带来了挑战。提出利用纠删码对秘密信息进行冗余预处理,再结合矩阵嵌入编码实现最小失真的隐写,从而建立了基于联合编码的嵌入和提取模型。在此基础上,分析了关键参数对联合编码性能的影响并给出了最优参数的选取算法。实验结果表明,所提联合编码能够有效提高隐写系统的抗分组丢失能力,且能减少对语音流的修改。
基金Projects(11661069,61763041) supported by the National Natural Science Foundation of ChinaProject(IRT_15R40) supported by Changjiang Scholars and Innovative Research Team in University,ChinaProject(2017TS045) supported by the Fundamental Research Funds for the Central Universities,China
文摘Most existing network representation learning algorithms focus on network structures for learning.However,network structure is only one kind of view and feature for various networks,and it cannot fully reflect all characteristics of networks.In fact,network vertices usually contain rich text information,which can be well utilized to learn text-enhanced network representations.Meanwhile,Matrix-Forest Index(MFI)has shown its high effectiveness and stability in link prediction tasks compared with other algorithms of link prediction.Both MFI and Inductive Matrix Completion(IMC)are not well applied with algorithmic frameworks of typical representation learning methods.Therefore,we proposed a novel semi-supervised algorithm,tri-party deep network representation learning using inductive matrix completion(TDNR).Based on inductive matrix completion algorithm,TDNR incorporates text features,the link certainty degrees of existing edges and the future link probabilities of non-existing edges into network representations.The experimental results demonstrated that TFNR outperforms other baselines on three real-world datasets.The visualizations of TDNR show that proposed algorithm is more discriminative than other unsupervised approaches.
基金Project(2009AA01A402) supported by the National High-Tech Research and Development Program of ChinaProject(NCET-06-0650) supported by Program for New Century Excellent Talents in University Project(IRT-0725) supported by Program for Changjiang Scholars and Innovative Research Team in Chinese University
文摘In order to optionally regulate embedding capacity and embedding transparency according to user's requirements in voice-over-IP(VoIP) steganography,a dynamic matrix encoding strategy(DMES) was presented.Differing from the traditional matrix encoding strategy,DMES dynamically chose the size of each message group in a given set of adoptable message sizes.The appearance possibilities of all adoptable sizes were set in accordance with the desired embedding performance(embedding rate or bit-change rate).Accordingly,a searching algorithm that could provide an optimal combination of appearance possibilities was proposed.Furthermore,the roulette wheel algorithm was employed to determine the size of each message group according to the optimal combination of appearance possibilities.The effectiveness of DMES was evaluated in StegVoIP,which is a typical covert communication system based on VoIP.The experimental results demonstrate that DMES can adjust embedding capacity and embedding transparency effectively and flexibly,and achieve the desired embedding performance in any case.For the desired embedding rate,the average errors are not more than 0.000 8,and the standard deviations are not more than 0.002 0;for the desired bit-change rate,the average errors are not more than 0.001 4,and the standard deviations are not more than 0.002 6.
