The great performance of deep learning in many visual tasks largely depends on the big data volume and the improvement of computing power. But in many practical projects, it is usually difficult to provide enough data to complete the task. Concerning the problem that the number of infrared images is small and the infrared images are hard to collect, a method to generate infrared images based on color images was proposed to obtain more infrared image data. Firstly, the existing color image and infrared image data were employed to construct the paired datasets. Secondly, the generator and the discriminator of Generative Adversarial Network (GAN) model were formed based on the convolutional neural network and the transposed convolutional neural network. Thirdly, the GAN model was trained based on the paired datasets until the Nash equilibrium between the generator and the discriminator was reached. Finally, the trained generator was used to transform the color image from the color field to the infrared field. The experimental results were evaluated based on quantitative evaluation metrics. The evaluation results show that the proposed method can generate high-quality infrared images. In addition, after the L1 or L2 regularization constraint was added to the loss function, the FID (Fréchet Inception Distance) score was respectively reduced by 23.95, 20.89 on average compared to the FID score of loss function not adding the constraint. As an unsupervised data augmentation method, the method can also be applied to many other visual tasks that lack train data, such as target recognition, target detection and data imbalance.

Aiming at the problems of low luminance, low contrast and poor visual information, a low-light image enhancement method based on simulating multi-exposure fusion was proposed. Firstly, the improved variational Retinex model and morphology were combined to generate the reference map to ensure the subject information in the exposed image set. Then, a new illumination compensation normalization function was constructed by combining Sigmoid function and gamma correction. At the same time, an unsharp masking algorithm based on Gaussian guided filtering was proposed to adjust the details of the reference map. Finally, the weighted values of exposed image set were designed from luminance, chromatic information and exposure rate respectively, and the final enhancement result was obtained through multi-scale fusion with effective avoidance of halo phenomenon and color distortion. The experimental results on different public datasets show that, compared with the traditional low-light image enhancement method, the proposed method has reduced the lightness distortion rate and increased the visual information fidelity. The proposed method can effectively preserve visual information, which is conducive to the real-time application of low-light image enhancement.

Aiming at the poor network embedding performance of cross-social network user alignment algorithm and the inability to guarantee the quality of negative samples generated by negative sampling method, a cross-social network KGEUA (Knowledge Graph Embedding User Alignment) algorithm was proposed. In the embedding stage, some known anchor user pairs were used for the positive sample expansion, and the Near_K negative sampling method was proposed to generate negative examples. Finally, the two social networks were embedded into a unified low-dimensional vector space with the knowledge graph embedding method. In the alignment stage, the existing user similarity measurement method was improved, the proposed structural similarity was combined with the traditional cosine similarity to measure the user similarity jointly, and an adaptive threshold-based greedy matching method was proposed to align users. Finally, the newly aligned user pairs were added to the training set to continuously optimize the vector space. The experimental results show that the proposed algorithm has the hits@30 value of 67.7% on the Twitter-Foursquare dataset, which is 3.3 to 34.8 percentage points higher than that of the state-of-the-art algorithm, improving the user alignment performance effectively.

Traditional feature selection methods have great limitations in the imbalanced text sentiment tendency classification, which are mainly reflected in the high feature dimension, the sparse characteristics, and the imbalanced feature distribution, making the reduction of classification accuracy. According to the distribution of emotional features of imbalanced texts, a Three-Way Decisions-Feature Selection algorithm (TWD-FS) was proposed for imbalanced text sentiment classification based on three-way decisions. In order to reduce the number of feature words and reduce the feature dimension, two supervised feature selection methods were combined, and the feature words selected were further filtered in order to make them satisfy the characteristics of the maximum between-class scatter degree and the minimum within-class scatter degree. In addition, the imbalance of sentiment features was decreased and the classification accuracy of minority sentiment was effectively improved by combining positive and negative sentiment features. The experimental results on COAE2013 Chinese microblog imbalanced datasets and other datasets show that the proposed feature selection algorithm TWD-FS can effectively improve the accuracy of imbalanced text sentiment classification.

Since a large number of Trojans are easily tracable on the Internet, a new Trojan attack scheme based on multimedia document was proposed. Firstly, the Trojan program was embedded into a carrier image as secret data by steganography. After the Trojan program was successfully injected, the encrypted user information was also hidden into the carrier image by steganography. Then the host automatically uploaded pictures to a social network. Finally, the attacker downloaded images from the social network and extracted secret data from images. The theoretical analysis and simulation results show that the proposed JPEG image steganography algorithm has good performance, and the Trojan scheme based on it outperfoms some existing algorithms in concealment, anti-forensics, anti-tracking and penetrating auditing. Such Trojans in social networks can cause user privacy leaks, so some precautions are given at last.

To improve the performance of speaker recognition system, a new method of feature extraction was proposed based on Mel Frequency Cepstrum Coefficient (MFCC) and Inverted MFCC (IMFCC). This method constructed a mixed feature by combining MFCC with IMFCC using Fisher criterion. The experimental results show that the mixed feature proposed in this paper has better recognition performance compared with MFCC not only in the pure voice database but also in the noisy environments.

In view of the requirement of convenience and fast speed of automobile ECU online upgrading, by researching the CAN bus communication as well as the IAP technology, the online upgrading method is designed based on software trigger. This method achieves the rapid online upgrading of ECU in automobile network by sending the instruction through online upgrading software to communicate with CAN bus. Thus it solves the operation inflexibility brought by the hardware trigger during the online upgrading. The upgrading system uses STM8AF51AA micro controller as the platform, and is implemented and applied in automotive BCM controller, which verifies the feasibility and reliability of this technology.

An efficient and credible authentication schema was constructed based on the visual cryptography. It avoided the disadvantages of traditional cryptography by adopting only two cryptography components: visual cryptography and MAC, and the safety has not been lowered down. Bar code was introduced into this schema as secret image to reduce the complexity and difficulty of the server's auto-recognition of secret information which was hidden in images, so that made the schema more efficient. In the end of this paper, we analyzed the schema's safety, and the result showed that the new schema can resist common attack effectively.