Handwritten Signature Spoofing With Conditional Generative Adversarial Nets

Generative adversarial networks have been a highly focused research topic in computer vision, especially in image synthesis and image-to-image translation. There are a lot of variations in generative nets, and different GANs are suitable for different applications. In this chapter, the authors investigated conditional generative adversarial networks to generate fake images, such as handwritten signatures. The authors demonstrated an implementation of conditional generative adversarial networks, which can generate fake handwritten signatures according to a condition vector tailored by humans.

Bidirectional Face Aging Synthesis Based on Improved Deep Convolutional Generative Adversarial Networks

The use of computers to simulate facial aging or rejuvenation has long been a hot research topic in the field of computer vision, and this technology can be applied in many fields, such as customs security, public places, and business entertainment. With the rapid increase in computing speeds, complex neural network algorithms can be implemented in an acceptable amount of time. In this paper, an optimized face-aging method based on a Deep Convolutional Generative Adversarial Network (DCGAN) is proposed. In this method, an original face image is initially mapped to a personal latent vector by an encoder, and then the personal potential vector is combined with the age condition vector and the gender condition vector through a connector. The output of the connector is the input of the generator. A stable and photo-realistic facial image is then generated by maintaining personalized facial features and changing age conditions. With regard to the objective function, the single adversarial loss of the Generated Adversarial Network (GAN) with the perceptual similarity loss is replaced by the perceptual similarity loss function, which is the weighted sum of adversarial loss, feature space loss, pixel space loss, and age loss. The experimental results show that the proposed method can synthesize an aging face with rich texture and visual reality and outperform similar work.

Research on Anti-Plagiarism in the Education Informatization Background

With the widely promotion of education informationization, education workers all around the world are facing a challenge that the electronic homework plagiarism has become increasingly serious. Our research includes technology innovation and education innovation, the technology innovation will divide plagiarism into two types, by using different algorithms to analysis and identify the plagiarism under the computer room environment and network copying, and the digital watermarking technology is applied to electronic operations against plagiarism. In network condition, vector space algorithm and edit distance algorithm is used to judge the similarity between electronic assignments and related documents. In the two years of teaching practice, we also strengthened credit education and reformed teaching methods, the percentage of plagiarism was decreased obviously.

GA-SVR Based Bearing Condition Degradation Prediction

A genetic algorithm-support vector regression model (GA-SVR) is proposed for machine performance degradation prediction. The main idea of the method is firstly to select the condition-sensitive features extracted from rolling bearing vibration signals using Genetic Algorithm to form a condition vector. Then prediction model is established for each feature time series. And the third step is to establish support vector regression models to obtain prediction result in each series. Finally, the condition prognosis can be obtained through combing all components to form a condition vector. Vibration data from a rolling bearing bench test process are used to verify accuracy of the proposed method. The results show that the model is an effective prediction method with a higher speed and a better accuracy.