Generative Adversarial Networks (GANs) have achieved impressive results in various image synthesis tasks, and are becoming a hot topic in computer vision research because of the impressive performance they achieved in various applications. In this paper, we introduce the recent research on GANs in the field of image processing, including image synthesis, image generation, image semantic editing, image-to-image translation, image super-resolution, image inpainting, and cartoon generation. We analyze and summarize the methods used in these applications which have improved the generated results. Then, we discuss the challenges faced by GANs and introduce some methods to deal with these problems. We also preview some likely future research directions in the field of GANs, such as video generation, facial animation synthesis and 3D face reconstruction. The purpose of this review is to provide insights into the research on GANs and to present the various applications based on GANs in different scenarios.

Blockchain, as one of the most promising technology, has attracted tremendous attention. The interesting characteristics of blockchain are decentralized ledger and strong security, while non-repudiation is the important property of information security in blockchain. A digital signature scheme is an effective approach to achieve non-repudiation. In this paper, the characteristics of blockchain and the digital signature to guarantee information non-repudiation are firstly discussed. Secondly, the typical digital signature schemes in blockchain are classified and analyzed, and then the state-of-the-art digital signatures are investigated and compared in terms of application fields, methods, security, and performance. Lastly, the conclusions are given, and some future works are suggested to stir research efforts in this field. Our works will facilitate to design efficient and secure digital signature algorithms in blockchain.

The authenticity and integrity of sensed data in the data collection stage is very critical aspect for the smart city industrial environment. They impact on the accuracy of data analysis and the objectivity of making decision. However, how to distinguish attack behaviors from interference in complex environments, and establish secure route to transmit data for resource-constrained terminals are the challenging problems. To address these problems, in this paper, we propose a trust-based security system (TSS). In TSS, we first design a trust model using binomial distribution for calculating the node's trust value and a third-party recommendation scheme for improving the objectivity of trust value. Then, we propose a trust management scheme for preventing the On-Off attack. After that, we design a secure routing protocol, which is used to balance the security, transmission performance and energy efficiency. Finally, the analytical results of the TSS are evaluated with extensive simulation experiment.

The MuralDH dataset is an invaluable digital resource developed for the conservation and restoration of Dunhuang murals, which are critical components of global cultural heritage facing threats from degradation. This dataset comprises over 5000 high-resolution images tailored to 512 × 512 pixels, emphasizing the preservation of mural integrity and detail. It includes 1000 images with pixel-level damage annotations for segmentation research and 500 images specially processed for super-resolution studies, catering to a wide range of digital restoration needs. While the primary focus of this work is the dataset itself, we also introduce a supportive digital restoration framework. This framework, which encompasses damage segmentation, inpainting, and super-resolution techniques, serves as a secondary validation of MuralDH’s utility and versatility. Through MuralDH, technology revives ancient art, embodying the essence of interdisciplinary innovation. By facilitating advanced research in computer vision and artificial intelligence, MuralDH aims to revolutionize the digital preservation practices for murals and other cultural artifacts, demonstrating the critical role of interdisciplinary collaboration in safeguarding our cultural legacy.