This paper proposes a novel paradigm for facial privacy protection that unifies multiple characteristics including anonymity, diversity, reversibility and security within a single lightweight framework. We name it PRO-Face S, short for Privacy-preserving Reversible Obfuscation of Face images via Secure flow-based model. In the framework, an Invertible Neural Network (INN) is utilized to process the input image along with its pre-obfuscated form, and generate the privacy protected image that visually approximates to the pre-obfuscated one, thus ensuring privacy. The pre-obfuscation applied can be in diversified form with different strengths and styles specified by users. Along protection, a secret key is injected into the network such that the original image can only be recovered from the protection image via the same model given the correct key provided. Two modes of image recovery are devised to deal with malicious recovery attempts in different scenarios. Finally, extensive experiments conducted on three public image datasets demonstrate the superiority of the proposed framework over multiple state-of-the-art approaches.
Regarding to the electric vehicle (EV) with dual-energy storage system (DESS) composed of batteries and ultra-capacitors, study on the structure and drive modes of DESS, after a detailed analysis of energy storage system based on power, resistance and constraints in driving, establish a mathematical model of energy management system of EV with DESS, and an energy management based on the fuzzy control strategy is designed. Finally, a simulation of EV with DESS by using ADVISOR simulation platform is studied, whose results show that the EV with DESS based on fuzzy control strategy can be more effective in distributing power between energy storage systems, and the dynamic performance as well as economic efficiency are also improved
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