Most of the current video playback speed recognition algorithms have poor extraction accuracy and many model parameters. Aiming at these problems， a dual-branch lightweight video playback speed recognition network was proposed. First， this network was a Three Dimensional （3D） convolutional network constructed on the basis of the SlowFast dual-branch network architecture. Secondly， in order to deal with the large number of parameters and many floating-point operations of S3D-G （Separable 3D convolutions network with Gating mechanism） network in video playback speed recognition tasks， a lightweight network structure adjustment was carried out. Finally， the Efficient Channel Attention （ECA） module was introduced in the network structure to generate the channel range corresponding to the focused content through the channel attention module， which helped to improve the accuracy of video feature extraction. In experiments， the proposed network was compared with S3D-G， SlowFast networks on the Kinetics-400 dataset. Experimental results show that with similar accuracy， the proposed network reduces both model size and model parameters by about 96% compared to SlowFast network， and the number of floating-point operations of the network is reduced to 5.36 GFLOPs， which means the running speed is increased significantly.

A ResNet-based faked sample detection algorithm was proposed for the detection of faked samples in audio scenes with low faking cost and undetectable sound replacement. The Constant Q Cepstral Coefficient （CQCC） features of the audio were extracted firstly， then the input features were learnt by the Residual Network （ResNet） structure， by combining the multi-layer residual blocks of the network and feature normalization， the classification results were output finally. On TIMIT and Voicebank databases， the highest detection accuracy of the proposed algorithm can reach 100%， and the lowest false acceptance rate of the algorithm can reach 1.37%. In realistic scenes， the highest detection accuracy of this algorithm is up to 99.27% when detecting the audios recorded by three different recording devices with the background noise of the device and the audio of the original scene. Experimental results show that it is effective to use the CQCC features of audio to detect the scene replacement trace of audio.

Echo hiding is a steganographic technique with audio as carrier. Currently， the steganalysis methods for echo hiding mainly use the cepstral coefficients as handcrafted-features to realize classification. However， when the echo amplitude is low， the detection performance of these traditional methods is not high. Aiming at the low echo amplitude condition， a steganalysis method for echo hiding based on Convolutional Neural Network （CNN） was proposed. Firstly， Short-Time Fourier Transform （STFT） was used to extract the amplitude spectrum coefficient matrix as the shallow feature. Secondly， the deep feature was extracted by the designed CNN framework from the shallow feature. The network framework consisted of four convolutional blocks and three fully connected layers. Finally， the classification results were output by Softmax. The proposed method was steganographically evaluated on three classic echo hiding algorithms. Experimental results indicate that the detection rates of the proposed method under low echo amplitude are 98.62%， 98.53% and 93.20% respectively. Compared with the existing traditional handcrafted-features based methods and deep learning based methods， the proposed method has the detection performance improved by more than 10%.

To solve the problem of optimization learning models in Belief Rule Base (BRB), a new parameter training approach based on the Particle Swarm Optimization (PSO) algorithm was proposed, which is one of the swarm intelligence algorithms. The optimization learning model was converted to nonlinear optimization problem with constraints. During the optimization process, all particles were limited in the search space and the particles with no speed were given velocity in order to maintain the diversity of the population of particles and achieve parameter training. In the practical pipeline leak detection problem, the Mean Absolute Error (MAE) of the trained system was 0.166478. The experimental results show the proposed method has good accuracy and it can be used for parameter training.

Concerning low classification accuracy and high false alarm rate of current intrusion detection models, an intrusion detection classification model based on Landmark ISOmetric MAPping (LISOMAP) and Deep First Search (DFS) Relevant Vector Machine (RVM) was proposed. The LISOMAP was adopted to reduce the dimension of the training data, and RVM based on the DFS was used for classification detection. Compared with the Principal Components Analysis (PCA)-Supported Vector Machine (SVM), the experimental results indicate that the LISOMAP-DFSRVM model has lower false alarm rate with almost the same detection rate.