To protect the information of video stream, many selective video encryption schemes have been proposed based on the H.265/HEVC video. However, most of the existing algorithms are not robust, thus failing to decrypt under packet loss. To further improve the robustness capability of video protection, a robust selective encryption scheme is proposed in this paper. In H.265/HEVC standard, video is encoded into multiple slices, and the slices are decoded independently. Inspired by the feature, each slice is individually encrypted using RC4 stream cipher. The pseudorandom binary sequence (PRBS) for one slice is related to encoding parameters and the SHA-256 hash value of the corresponding slice header, thus ensuring the real-time update of the PRBS and increasing the resistance to chosen-plaintext attack. A two-rounds shifting algorithm is designed to scramble non-zero coefficients of the transform units (TUs), and then motion vector difference (MVD) parameters, quantized transform coefficients (QTCs) and intra prediction modes (IPMs) are selected for encryption. The combination of coefficient scrambling and syntax elements encryption further improves the encryption performance. The proposed scheme is slice-synchronized. Hence, it decrypts normally in the case of packet loss, and supports online real-time interaction. Furthermore, the simulation and analysis results show that the proposed algorithm has format compatibility, high security and low time complexity, which is a promising tool for video cryptographic applications.

The roller is the core component of the flexible material roll-to-roll equipment, and its performance will affect the processing quality, so it is necessary to predict the health of the roller. In order to improve the prediction accuracy and effectively extract the time sequence information hidden in the signal, a LSTM-SVM-based processing roll performance degradation prediction model is proposed. By collecting the bearing vibration sensor data, extracting the characteristics of the vibration signal after normalization, using the extracted features as the input of the prediction model, inputting a part of the samples as the training set into the LSTM-SVM prediction model, and inputting the model in batches for network training, and Adjustment parameters. After the model is trained, use the test set for testing. Compared with support vector machines, the SLTM-SVM model is more effective in predicting the performance degradation of roll-to-roll equipment.

In this study, a pioneer selective video encryption (PSVE) algorithm is proposed based on the pseudorandom number generator (PRNG) of the Zipf distribution (Z-PRNG). It is a general algorithm with high efficiency and security. The encryption process is completely separable from the video coding process. In the PSVE algorithm, Z-PRNG is designed based on the 3D SCL-HMC hyperchaotic map. Firstly, encapsulated byte sequence payloads (EBSPs) are extracted from the video bitstream. Secondly, random numbers of the Zipf distribution are generated by Z-PRNG, and they are used to randomly select encrypted data from each EBSP. Lastly, the extracted data are encrypted by AES-CTR to obtain the encrypted video. Compared with existing algorithms, the encryption position is more flexible, and the key space is further enhanced. The high efficiency video coding (HEVC) video and the advanced video coding (AVC) video are taken as examples to test the PSVE algorithm. The analysis results show that the proposed scheme can effectively resist common attacks, and its time complexity is much less than most existing algorithms.

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