Generative adversarial networks (GANs) are a hot research topic recently. GANs have been widely studied since 2014, and a large number of algorithms have been proposed. However, there is few comprehensive study explaining the connections among different GANs variants, and how they have evolved. In this paper, we attempt to provide a review on various GANs methods from the perspectives of algorithms, theory, and applications. Firstly, the motivations, mathematical representations, and structure of most GANs algorithms are introduced in details. Furthermore, GANs have been combined with other machine learning algorithms for specific applications, such as semi-supervised learning, transfer learning, and reinforcement learning. This paper compares the commonalities and differences of these GANs methods. Secondly, theoretical issues related to GANs are investigated. Thirdly, typical applications of GANs in image processing and computer vision, natural language processing, music, speech and audio, medical field, and data science are illustrated. Finally, the future open research problems for GANs are pointed out.

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Age progression and regression refers to aesthetically rendering a given face image to present effects of face aging and rejuvenation, respectively. Although numerous studies have been conducted in this topic, there are two major problems: 1) multiple models are usually trained to simulate different age mappings, and 2) the photo-realism of generated face images is heavily influenced by the variation of training images in terms of pose, illumination, and background. To address these issues, in this paper, we propose a framework based on conditional Generative Adversarial Networks (cGANs) to achieve age progression and regression simultaneously. Particularly, since face aging and rejuvenation are largely different in terms of image translation patterns, we model these two processes using two separate generators, each dedicated to one age changing process. In addition, we exploit spatial attention mechanisms to limit image modifications to regions closely related to age changes, so that images with high visual fidelity could be synthesized for in-the-wild cases. Experiments on multiple datasets demonstrate the ability of our model in synthesizing lifelike face images at desired ages with personalized features well preserved, and keeping age-irrelevant regions unchanged.

Abstract. Biometric cryptosystem can not only provide an efficient mechanism for template protection, but also facilitate cryptographic key management, thus becomes a promising direction in information security field. In this paper, we propose a robust key extraction approach which consists of concatenated coding scheme and bit masking scheme based on iris database. The concatenated coding scheme that combines Reed-Solomon code and convolutional code is proposed so that much longer keys can be extracted from the iris data, while the bit masking scheme is proposed to minimize and randomize the errors occur in the iris codes, making the error pattern more suitable for the coding scheme. The experiment results show that the system can achieve a FRR of 0.52% with the key length of 938 bits.

Artificial intelligence (AI) achieved important breakthroughs under the joint impetus of an unprecedented volume of data and exponentially growing computing capacities, and therefore became a significant focus of competition among the big powers. Meanwhile, AI showed its transformative impacts on human society and caused increasing public concern as well as considerable controversy. This article tries to draw a realistic panorama of AI at the macro level, including basic concepts, development history, current situation, and future trends; to reveal an unbiased profile and growing rules; and to develop a scientific perspective and realistic expectations of social knowledge. Finally, the article proposes an initiative to face the potential safety risks and challenges. This requires the strengthening of forward-looking prevention and guidance on restraint, the minimization of risk, and ensuring the safe, positive, and profitable development of AI.

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