This study examines motor cortical representation of hand position and its relationship to the representation of hand velocity during reaching movements. In all, 978 motor cortical neurons were recorded from the proximal arm area of rostral motor cortex. The results demonstrate that position and velocity are simultaneously encoded by single motor cortical neurons in an additive fashion and that the relative weights of the position and velocity signals change dynamically during reaching. The two variables--hand position and hand velocity--are highly correlated in the standard center-out reaching task. A new reaching task (standard reaching) is introduced to minimize these correlations. Likewise, a new decoding method (indirect OLE) was developed to analyze the data by simultaneously decoding both three-dimensional (3D) hand position and 3D hand velocity from correlated neural activity. This method shows that, on average, the reconstructed velocity led the actual hand velocity by 122 ms, whereas the reconstructed position signal led the actual hand position by 81 ms.

In this paper, a novel strategy of Secure Steganograpy based on Generative Adversarial Networks is proposed to generate suitable and secure covers for steganography. The proposed architecture has one generative network, and two discriminative networks. The generative network mainly evaluates the visual quality of the generated images for steganography, and the discriminative networks are utilized to assess their suitableness for information hiding. Different from the existing work which adopts Deep Convolutional Generative Adversarial Networks, we utilize another form of generative adversarial networks. By using this new form of generative adversarial networks, significant improvements are made on the convergence speed, the training stability and the image quality. Furthermore, a sophisticated steganalysis network is reconstructed for the discriminative network, and the network can better evaluate the performance of the generated images. Numerous experiments are conducted on the publicly available datasets to demonstrate the effectiveness and robustness of the proposed method.

Recently GAN generated face images are more and more realistic with high-quality, even hard for human eyes to detect. On the other hand, the forensics community keeps on developing methods to detect these generated fake images and try to ensure the credibility of visual contents. Although researchers have developed some methods to detect generated images, few of them explore the important problem of generalization ability of forensics model. As new types of GANs are emerging fast, the generalization ability of forensics models to detect new types of GAN images is absolutely an essential research topic, which is also very challenging. In this paper, we explore this problem and propose to use preprocessed images to train a forensic CNN model. By applying similar image level preprocessing to both real and fake images, unstable low level noise cues are destroyed, and the forensics model is forced to learn more intrinsic features to classify the generated and real face images. Our experimental results also prove the effectiveness of the proposed method.