Aiming at the motion control problem of two-link manipulator, a new control method based on deep reinforcement learning was proposed. Firstly, the simulation environment of manipulator was built, which includes the two-link manipulator, target and obstacle. Then, according to the target setting, state variables as well as reward and punishment mechanism of the environment model, three kinds of deep reinforcement learning models were established for training. Finally, the motion control of the two-link manipulator was realized. After comparing and analyzing the three proposed models, Deep Deterministic Policy Gradient (DDPG) algorithm was selected for further research to improve its applicability, so as to shorten the debugging time of the manipulator model, and avoided the obstacle to reach the target smoothly. Experimental results show that, the proposed deep reinforcement learning method can effectively control the motion of two-link manipulator, the improved DDPG algorithm control model has the convergence speed increased by two times and the stability after convergence enhances. Compared with the traditional control method, the proposed deep reinforcement learning control method has higher efficiency and stronger applicability.

For the distortion of the image after embedding secret is excessively obvious, a new Reversible Data Hiding (RDH) based on Pixel Value Order (PVO) was proposed. Firstly, the pixels of a carrier image were divided into gray and white layers, the pixels of a gray layer were selected as the target pixels, and the four white pixels in the cross positions of the target pixels were sorted. Secondly, according to the sorting result, the mean value of the two end pixels and the mean value of the median pixels were calculated, and the reversible constraint was used to achieve dynamic prediction of pixels. Finally, a Prediction Error Histogram (PEH) was constructed according to the prediction result. Six images in the USC-SIPI standard image library were used for simulation experiments. The experimental results show that, when the Embedding Capacity (EC) is 10000 b and the average Peak Signal-to-Noise Ratio (PSNR) is 61.89 dB, the proposed algorithm can effectively reduce the distortion of the image with ciphertext.

Aiming at enhancing the embedded capacity and enriching the available encryption algorithm of reversible data hiding in encrypted domain, a new scheme was proposed by adopting double-level encryption methods and embedding the secret information based on Code Division Multiple Access (CDMA). The image was first divided into blocks and a multi-granularity encryption was introduced. The image was first divided into blocks, which were scrambled by introducing multi-granularity encryption, then 2 bits in the middle of each pixel in blocks were encrypted by a stream cipher. Based on the idea of CDMA, k mutually orthogonal matrices of 4 bits were selected to carry k-level secret information. The orthogonal matrices can guarantee the multi-level embedding and improve the embedding capacity. The pseudo bit was embedded into the blocks that cannot meet the embedding condition. The secret data could be extracted by using the extraction key; the original image could be approximately recovered by using the image decryption key; with both of the keys, the original image could be recovered losslessly. Experimental results show that, when the Peak Signal-to-Noise Ratio (PSNR) of gray Lena image of 512×512 pixels is higher than 36 dB, the maximum embedded capacity of the proposed scheme is 133313 bit. The proposed scheme improves the security of encrypted images and greatly enhances the embedded capacity of reversible information in ciphertext domain while ensuring the reversibility.