In most word-level lipreading architectures of recent years, temporal feature extraction module tend to employ Multi-scale Temporal Convolution Network (MS-TCN). In our experiments, we have noticed it is hard for MS-TCN to deal with noise information that may contain in image sequences. In order to solve the problems, we propose a lipreading architecture based on temporal shrinkage network and NetVLAD. We first propose Temporal Shrinkage Unit according to Residual Shrinkage Network and then replace temporal convolution unit with it. The improved network which named Multi-scale Temporal Shrinkage Network (MS-TSN) could focus more on relevant information. Following with MS-TSN that deals with noise frames, NetVLAD is proposed to integrate local information into global feature. Compared with Global Average Pooling, NetVLAD could extract key features by clustering. Our experiments on Lipreading in the Wild (LRW) show that the architecture we propose achieves an accuracy of 89.41%, attaining new state-of-the-art in word-level lipreading. In addition, we build a new Mandarin Chinese lipreading dataset named MCLR-100 and verify our proposed architecture on it.

High-accuracy, low-latency gaze tracking is becoming one of the indispensable features in augmented reality (AR) head-mounted devices (HMDs). Researchers have proposed different approaches to predict gaze positions from eye images. However, since only the eye modality is focused, these appearance-based algorithms are still struggle to trade off the accuracy and running speed in HMDs. In this paper, we propose a lightweight multi-modal network (HE-Tracker) to regress gaze positions. By fusing head-movement features with eye features, HE-Tracker achieves comparable accuracy (3.655° in all subjects) and $27 \times$ speedup (48 fps in the specialized AR HMD) compared to the state-of-the-art gaze tracking algorithm. We further demonstrate that when applying our head-eye coordination strategy to other baseline models, all these models achieve at least 6.36% performance improvement without a pronounced effect on running speed. Moreover, we construct HE-Gaze, the first multi-modal dataset with eye images and head-movement data for near-eye gaze tracking. This dataset is currently made of 757,360 frames and 15 persons, providing an opportunity to foster research in multi-modal gaze tracking approaches. Our dataset is available at DOWNLOAD LINK1.

The detection of LSB steganography is a question of common interest in the research of steganalysis techniques. In this paper, the distribution of the difference between the current pixel value and its neighborhood average pixel value is statistically modeled, and then the variance of this statistical distribution is defined as a measurement of image smoothness. Based on the analysis of the effects on the image smoothness brought by message embedding and LSB plane flipping, a new steganalytic technique capable of reliable detection of spatial LSB steganography is proposed. The algorithm can exactly estimate the amount of hidden messages and detect the existence of hidden messages embedded in the image simultaneously. Experimental results show that the proposed algorithm is effective