3D-Aware Adversarial Makeup Generation for Facial Privacy Protection.

The privacy and security of face data on social media are facing unprecedented challenges as it is vulnerable to unauthorized access and identification. A common practice for solving this problem is to modify the original data so that it could be protected from being recognized by malicious face recognition (FR) systems. However, such "adversarial examples" obtained by existing methods usually suffer from low transferability and poor image quality, which severely limits the application of these methods in real-world scenarios. In this paper, we propose a 3D-Aware Adversarial Makeup Generation GAN (3DAM-GAN). which aims to improve the quality and transferability of synthetic makeup for identity information concealing. Specifically, a UV-based generator consisting of a novel Makeup Adjustment Module (MAM) and Makeup Transfer Module (MTM) is designed to render realistic and robust makeup with the aid of symmetric characteristics of human faces. Moreover, a makeup attack mechanism with an ensemble training strategy is proposed to boost the transferability of black-box models. Extensive experiment results on several benchmark datasets demonstrate that 3DAM-GAN could effectively protect faces against various FR models, including both publicly available state-of-the-art models and commercial face verification APIs, such as Face++, Baidu, and Aliyun.

Earning Extra Performance From Restrictive Feedbacks.

Many machine learning applications encounter situations where model providers are required to further refine the previously trained model so as to gratify the specific need of local users. This problem is reduced to the standard model tuning paradigm if the target data is permissibly fed to the model. However, it is rather difficult in a wide range of practical cases where target data is not shared with model providers but commonly some evaluations about the model are accessible. In this paper, we formally set up a challenge named Earning eXtra PerformancE from restriCTive feEDdbacks (EXPECTED) to describe this form of model tuning problems. Concretely, EXPECTED admits a model provider to access the operational performance of the candidate model multiple times via feedback from a local user (or a group of users). The goal of the model provider is to eventually deliver a satisfactory model to the local user(s) by utilizing the feedbacks. Unlike existing model tuning methods where the target data is always ready for calculating model gradients, the model providers in EXPECTED only see some feedbacks which could be as simple as scalars, such as inference accuracy or usage rate. To enable tuning in this restrictive circumstance, we propose to characterize the geometry of the model performance with regard to model parameters through exploring the parameters' distribution. In particular, for deep models whose parameters distribute across multiple layers, a more query-efficient algorithm is further tailor-designed that conducts layerwise tuning with more attention to those layers which pay off better. Our theoretical analyses justify the proposed algorithms from the aspects of both efficacy and efficiency. Extensive experiments on different applications demonstrate that our work forges a sound solution to the EXPECTED problem, which establishes the foundation for future studies towards this direction.

Online Mental Fatigue Monitoring via Indirect Brain Dynamics Evaluation.

Driver mental fatigue leads to thousands of traffic accidents. The increasing quality and availability of low-cost electroencephalogram (EEG) systems offer possibilities for practical fatigue monitoring. However, non-data-driven methods, designed for practical, complex situations, usually rely on handcrafted data statistics of EEG signals. To reduce human involvement, we introduce a data-driven methodology for online mental fatigue detection: self-weight ordinal regression (SWORE). Reaction time (RT), referring to the length of time people take to react to an emergency, is widely considered an objective behavioral measure for mental fatigue state. Since regression methods are sensitive to extreme RTs, we propose an indirect RT estimation based on preferences to explore the relationship between EEG and RT, which generalizes to any scenario when an objective fatigue indicator is available. In particular, SWORE evaluates the noisy EEG signals from multiple channels in terms of two states: shaking state and steady state. Modeling the shaking state can discriminate the reliable channels from the uninformative ones, while modeling the steady state can suppress the task-nonrelevant fluctuation within each channel. In addition, an online generalized Bayesian moment matching (online GBMM) algorithm is proposed to online-calibrate SWORE efficiently per participant. Experimental results with 40 participants show that SWORE can maximally achieve consistent with RT, demonstrating the feasibility and adaptability of our proposed framework in practical mental fatigue estimation.

Geographic patterns of insect diversity across China's nature reserves: The roles of niche conservatism and range overlapping.

AIM: Insects are the most species-rich clade in the world, but the broad-scale diversity pattern and the potential drivers have not been well documented for the clade as a whole. We aimed to examine the relative roles of contemporary and historical climate, niche conservatism, range overlapping, and other environmental factors on geographic patterns of species richness and phylogenetic structure, for insects across China. LOCATION: China. METHODS: We collected insect data from 184 nature reserves and examined geographic patterns of species richness and mean root distance (MRD, a metric of the evolutionary development of assemblages) for different biogeographic affinities (Palearctic, Oriental, and widespread species) and for clades originated during the warm and cold geohistorical periods ("warm clades" and "cold clades," respectively). We related richness and MRD to contemporary and historical climate, area, habitat heterogeneity, and human disturbance to evaluate their relative importance. RESULTS: Total species richness revealed a hump-shaped latitudinal pattern, peaking between 30°~35°N. Richness patterns differed markedly among evolutionary groups: Oriental species richness decreased significantly with higher latitude but Palearctic species increased, while other groups again peaked between 30°~35°N. The range overlapping of different biogeographic groups in midlatitudes may be an important contributor to humped latitudinal richness patterns. MRD was positively related to latitude and increased more rapidly for "warm clades" than "cold clades." Historical climate factors (especially winter coldness) were among the strongest predictors for both richness and phylogenetic patterns, for each evolutionary group, suggesting the strong influence of niche conservatism. CONCLUSIONS: The hump-shaped latitudinal pattern of insect richness in China is mainly shaped by niche conservatism and range overlapping, supplemented by habitat heterogeneity and contemporary climate. The role of niche conservatism and range overlapping may have been overlooked if only total species richness was analyzed, suggesting the importance of examining different evolutionary groups separately.