针对基于标记编码的加密图像可逆数据隐藏存在图像冗余未充分利用和信息泄露问题,提出一种基于MSB(Most Significant Bit)二维标记的加密图像可逆数据隐藏(Reversible Data Hiding in Encrypted Image,RDH-EI)算法.为提高算法的嵌入容量...针对基于标记编码的加密图像可逆数据隐藏存在图像冗余未充分利用和信息泄露问题,提出一种基于MSB(Most Significant Bit)二维标记的加密图像可逆数据隐藏(Reversible Data Hiding in Encrypted Image,RDH-EI)算法.为提高算法的嵌入容量,在二维标记图生成阶段,根据原始与预测像素值构造出差异序列,生成MSB二维标记(l1,l2).第一维l1和第二维l2分别记录原始与预测像素值初始连续相同MSBs位数和后继连续相反MSBs(Subsequent Consecutive Opposite MSBs,SCO-MSBs)位数.SCO-MSBs的使用提高像素冗余的利用率,结合范式哈夫曼编码实现嵌入容量的提升.为提高算法的安全性,在伪标记图与加密图像构造阶段,将二维标记图生成的编码流与保存所有辅助信息的额外数据流进行有效信息合并生成原始流后加密,同时在构造加密图像过程中生成用于标识可嵌入位置的伪标记图.原始流加密能有效防止图像信息泄露,伪标记图则用于确定嵌入的预留空间位置.实验结果表明,与现有同类算法相比,本文算法能防止标记图泄露并抵抗唯密文攻击,嵌入容量提高0.208 bpp以上,且算法实现完全可逆的同时,运行时间将近现有算法的1/4.展开更多
Motor imagery(MI)based electroencephalogram(EEG)represents a frontier in enabling direct neural control of external devices and advancing neural rehabilitation.This study introduces a novel time embedding technique,te...Motor imagery(MI)based electroencephalogram(EEG)represents a frontier in enabling direct neural control of external devices and advancing neural rehabilitation.This study introduces a novel time embedding technique,termed traveling-wave based time embedding,utilized as a pseudo channel to enhance the decoding accuracy of MI-EEG signals across various neural network architectures.Unlike traditional neural network methods that fail to account for the temporal dynamics in MI-EEG in individual difference,our approach captures time-related changes for different participants based on a priori knowledge.Through extensive experimentation with multiple participants,we demonstrate that this method not only improves classification accuracy but also exhibits greater adaptability to individual differences compared to position encoding used in Transformer architecture.Significantly,our results reveal that traveling-wave based time embedding crucially enhances decoding accuracy,particularly for participants typically considered“EEG-illiteracy”.As a novel direction in EEG research,the traveling-wave based time embedding not only offers fresh insights for neural network decoding strategies but also expands new avenues for research into attention mechanisms in neuroscience and a deeper understanding of EEG signals.展开更多
文摘Motor imagery(MI)based electroencephalogram(EEG)represents a frontier in enabling direct neural control of external devices and advancing neural rehabilitation.This study introduces a novel time embedding technique,termed traveling-wave based time embedding,utilized as a pseudo channel to enhance the decoding accuracy of MI-EEG signals across various neural network architectures.Unlike traditional neural network methods that fail to account for the temporal dynamics in MI-EEG in individual difference,our approach captures time-related changes for different participants based on a priori knowledge.Through extensive experimentation with multiple participants,we demonstrate that this method not only improves classification accuracy but also exhibits greater adaptability to individual differences compared to position encoding used in Transformer architecture.Significantly,our results reveal that traveling-wave based time embedding crucially enhances decoding accuracy,particularly for participants typically considered“EEG-illiteracy”.As a novel direction in EEG research,the traveling-wave based time embedding not only offers fresh insights for neural network decoding strategies but also expands new avenues for research into attention mechanisms in neuroscience and a deeper understanding of EEG signals.
