提出一种基于(7,4)汉明码纠错机制的抗视频帧操作的帧定位算法,可通过识别含水印帧来提取受到帧操作攻击的水印信息.首先利用改进的简单线性迭代聚类(simple linear iterative clustering,SLIC)超像素分割算法提取视频帧的固有特征X,这...提出一种基于(7,4)汉明码纠错机制的抗视频帧操作的帧定位算法,可通过识别含水印帧来提取受到帧操作攻击的水印信息.首先利用改进的简单线性迭代聚类(simple linear iterative clustering,SLIC)超像素分割算法提取视频帧的固有特征X,这里X是一串7位的0-1序列,是由SLIC超像素分割算法通过预处理、量化等运算得到的聚类中心.将视频帧的固有特征X与汉明码结合形成一个特殊的编码S.以编码S为定位码,利用(7,4)汉明码的纠错机制修改1位可隐藏3位的性能来实现定位码的标识,再基于DCT-SVD(discrete cosine transformation-singular value decomposition)分解将水印信息嵌入到含定位标识的视频中.实验结果表明,该算法不仅能抵抗帧删除、帧添加和重编码等攻击,也能抵抗常见的信号处理操作.展开更多
We propose a Rate-Distortion (RD) optimized strategy for frame-dropping and scheduling of multi-user conversa- tional and streaming videos. We consider a scenario where conversational and streaming videos share the fo...We propose a Rate-Distortion (RD) optimized strategy for frame-dropping and scheduling of multi-user conversa- tional and streaming videos. We consider a scenario where conversational and streaming videos share the forwarding resources at a network node. Two buffers are setup on the node to temporarily store the packets for these two types of video applications. For streaming video, a big buffer is used as the associated delay constraint of the application is moderate and a very small buffer is used for conversational video to ensure that the forwarding delay of every packet is limited. A scheduler is located behind these two buffers that dynamically assigns transmission slots on the outgoing link to the two buffers. Rate-distortion side information is used to perform RD-optimized frame dropping in case of node overload. Sharing the data rate on the outgoing link between the con- versational and the streaming videos is done either based on the fullness of the two associated buffers or on the mean incoming rates of the respective videos. Simulation results showed that our proposed RD-optimized frame dropping and scheduling ap- proach provides significant improvements in performance over the popular priority-based random dropping (PRD) technique.展开更多
文摘提出一种基于(7,4)汉明码纠错机制的抗视频帧操作的帧定位算法,可通过识别含水印帧来提取受到帧操作攻击的水印信息.首先利用改进的简单线性迭代聚类(simple linear iterative clustering,SLIC)超像素分割算法提取视频帧的固有特征X,这里X是一串7位的0-1序列,是由SLIC超像素分割算法通过预处理、量化等运算得到的聚类中心.将视频帧的固有特征X与汉明码结合形成一个特殊的编码S.以编码S为定位码,利用(7,4)汉明码的纠错机制修改1位可隐藏3位的性能来实现定位码的标识,再基于DCT-SVD(discrete cosine transformation-singular value decomposition)分解将水印信息嵌入到含定位标识的视频中.实验结果表明,该算法不仅能抵抗帧删除、帧添加和重编码等攻击,也能抵抗常见的信号处理操作.
基金Project (No. STE1093/1-1) supported by the German ResearchFoundation, Germany
文摘We propose a Rate-Distortion (RD) optimized strategy for frame-dropping and scheduling of multi-user conversa- tional and streaming videos. We consider a scenario where conversational and streaming videos share the forwarding resources at a network node. Two buffers are setup on the node to temporarily store the packets for these two types of video applications. For streaming video, a big buffer is used as the associated delay constraint of the application is moderate and a very small buffer is used for conversational video to ensure that the forwarding delay of every packet is limited. A scheduler is located behind these two buffers that dynamically assigns transmission slots on the outgoing link to the two buffers. Rate-distortion side information is used to perform RD-optimized frame dropping in case of node overload. Sharing the data rate on the outgoing link between the con- versational and the streaming videos is done either based on the fullness of the two associated buffers or on the mean incoming rates of the respective videos. Simulation results showed that our proposed RD-optimized frame dropping and scheduling ap- proach provides significant improvements in performance over the popular priority-based random dropping (PRD) technique.