The two underlying requirements of face age progression, i.e. aging accuracy and identity permanence, are not well studied in the literature. In this paper, we present a novel generative adversarial network based approach. It separately models the constraints for the intrinsic subjectspecific characteristics and the age-specific facial changes with respect to the elapsed time, ensuring that the generated faces present desired aging effects while simultaneously keeping personalized properties stable. Further, to generate more lifelike facial details, high-level age-specific features conveyed by the synthesized face are estimated by a pyramidal adversarial discriminator at multiple scales, which simulates the aging effects in a finer manner. The proposed method is applicable to diverse face samples in the presence of variations in pose, expression, makeup, etc., and remarkably vivid aging effects are achieved. Both visual fidelity and quantitative evaluations show that the approach advances the state-of-the-art.

The modern intelligent transportation system brings not only new opportunities for vehicular Internet of Things (IoT) services but also new challenges for vehicular ad-hoc networks (VANETs). Apart from enhanced network performance, a practical and reliable security scheme is needed to handle the trust management while preserving user privacy at the same time. The emerging 5G mobile communication system is viewed as a prominent technology for ultra-reliable, low-latency wireless communication services. Furthermore, incorporating software-defined network (SDN) architecture into the 5G-VANET enables global information gathering and network control. Hence, real-time IoT services on transportation monitoring and reporting can be well supported. Both pave the way for an innovative vehicular security scheme. This paper investigates the security and privacy issue in the transportation system and the vehicular IoT environment in SDN-enabled 5G-VANET. Due to the decentralized and immutable characteristics of blockchain, a blockchain-based security framework is designed to support the vehicular IoT services, i.e., real-time cloud-based video report and trust management on vehicular messages. This paper explicitly illustrates the SDN-enabled 5G-VANET model and the scheduling procedures of the blockchain-based framework. The numerical simulation results also show that malicious vehicular nodes or messages can be well detected while the overhead and impact on the network performance are acceptable for large-scale scenarios. Through case studies and theoretical analysis, we demonstrate our design substantially guarantees a secure and trustworthy vehicular IoT environment with user privacy preserved.

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Face aging simulation has received rising investigations nowadays, whereas it still remains a challenge to generate convincing and natural age-progressed face images. In this paper, we present a novel approach to such an issue using hidden factor analysis joint sparse representation. In contrast to the majority of tasks in the literature that integrally handle the facial texture, the proposed aging approach separately models the person-specific facial properties that tend to be stable in a relatively long period and the age-specific clues that gradually change over time. It then transforms the age component to a target age group via sparse reconstruction, yielding aging effects, which is finally combined with the identity component to achieve the aged face. Experiments are carried out on three face aging databases, and the results achieved clearly demonstrate the effectiveness and robustness of the proposed method in rendering a face with aging effects. In addition, a series of evaluations prove its validity with respect to identity preservation and aging effect generation.