Reversible data hiding in encrypted images based on additive secret sharing and additive joint coding using an intelligent predictor

Reversible data hiding in encrypted images(RDHEI)is essential for safeguarding sensitive information within the encrypted domain.In this study,we propose an intelligent pixel predictor based on a residual group block and a spatial attention module,showing superior pixel prediction performance compared to existing predictors.Additionally,we introduce an adaptive joint coding method that leverages bit-plane characteristics and intra-block pixel correlations to maximize embedding space,outperforming single coding approaches.The image owner employs the presented intelligent predictor to forecast the original image,followed by encryption through additive secret sharing before conveying the encrypted image to data hiders.Subsequently,data hiders encrypt secret data and embed them within the encrypted image before transmitting the image to the receiver.The receiver can extract secret data and recover the original image losslessly,with the processes of data extraction and image recovery being separable.Our innovative approach combines an intelligent predictor with additive secret sharing,achieving reversible data embedding and extraction while ensuring security and lossless recovery.Experimental results demonstrate that the predictor performs well and has a substantial embedding capacity.For the Lena image,the number of prediction errors within the range of[-5,5]is as high as 242500 and our predictor achieves an embedding capacity of 4.39 bpp.

An adaptive beamforming algorithm based on direction vector rotation and joint iterative optimization

An adaptive beamforming algorithm named robust joint iterative optimizationdirection adaptive （RJIO-DA） is proposed for large-array scenarios. Based on the framework of minimum variance distortionless response （MVDR）, the proposed algorithm jointly updates a transforming matrix and a reduced-rank filter. Each column of the transforming matrix is treated as an independent direction vector and updates the weight values of each dimension within a subspace. In addition, the direction vector rotation improves the performance of the algorithm by reducing the uncertainties due to the direction error. Simulation results show that the RJIO-DA algorithm has lower complexity and faster convergence than other conventional reduced-rank algorithms.

Research on Transfer Learning of Vision-based Gesture Recognition

Gesture recognition has been widely used for human-robot interaction.At present,a problem in gesture recognition is that the researchers did not use the learned knowledge in existing domains to discover and recognize gestures in new domains.For each new domain,it is required to collect and annotate a large amount of data,and the training of the algorithm does not benefit from prior knowledge,leading to redundant calculation workload and excessive time investment.To address this problem,the paper proposes a method that could transfer gesture data in different domains.We use a red-green-blue(RGB)Camera to collect images of the gestures,and use Leap Motion to collect the coordinates of 21 joint points of the human hand.Then,we extract a set of novel feature descriptors from two different distributions of data for the study of transfer learning.This paper compares the effects of three classification algorithms,i.e.,support vector machine(SVM),broad learning system(BLS)and deep learning(DL).We also compare learning performances with and without using the joint distribution adaptation(JDA)algorithm.The experimental results show that the proposed method could effectively solve the transfer problem between RGB Camera and Leap Motion.In addition,we found that when using DL to classify the data,excessive training on the source domain may reduce the accuracy of recognition in the target domain.