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.

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.

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.