Imperceptible Physical Attack against Face Recognition Systems via LED Illumination Modulation

Although face recognition starts to play an important role in our daily life, we need to pay attention that data-driven face recognition vision systems are vulnerable to adversarial attacks. However, the current two categories of adversarial attacks, namely digital attacks and physical attacks both have drawbacks, with the former ones impractical and the latter one conspicuous, high-computational and inexecutable. To address the issues, we propose a practical, executable, inconspicuous and low computational adversarial attack based on LED illumination modulation. To fool the systems, the proposed attack generates imperceptible luminance changes to human eyes through fast intensity modulation of scene LED illumination and uses the rolling shutter effect of CMOS image sensors in face recognition systems to implant luminance information perturbation to the captured face images. In summary,we present a denial-of-service (DoS) attack for face detection and a dodging attack for face verification. We also evaluate their effectiveness against well-known face detection models, Dlib, MTCNN and RetinaFace , and face verification models, Dlib, FaceNet,and ArcFace.The extensive experiments show that the success rates of DoS attacks against face detection models reach 97.67%, 100%, and 100%, respectively, and the success rates of dodging attacks against all face verification models reach 100%

Mesoscale convective systems in the third pole region: Characteristics, mechanisms and impact on precipitation

The climate system of the Third Pole region, including the (TP) and its surroundings, is highly sensitive to global warming. Mesoscale convective systems (MCSs) are understood to be a vital component of this climate system. Driven by the monsoon circulation, surface heating, and large-scale and local moisture supply, they frequently occur during summer and mostly over the central and eastern TP as well as in the downstream regions. Further, MCSs have been highlighted as important contributors to total precipitation as they are efficient rain producers affecting water availability (seasonal precipitation) and potential flood risk (extreme precipitation) in the densely populated downstream regions. The availability of multi-decadal satellite observations and high-resolution climate model datasets has made it possible to study the role of MCSs in the under-observed TP water balance. However, the usage of different methods for MCS identification and the different focuses on specific subregions currently hamper a systematic and consistent assessment of the role played by MCSs and their impact on precipitation over the TP headwaters and its downstream regions. Here, we review observational and model studies of MCSs in the TP region within a common framework to elucidate their main characteristics, underlying mechanisms, and impact on seasonal and extreme precipitation. We also identify major knowledge gaps and provide suggestions on how these can be addressed using recently published high-resolution model datasets. Three important identified knowledge gaps are 1) the feedback of MCSs to other components of the TP climate system, 2) the impact of the changing climate on future MCS characteristics, and 3) the basin-scale assessment of flood and drought risks associated with changes in MCS frequency and intensity. A particularly promising tool to address these knowledge gaps are convection-permitting climate simulations. Therefore, the systematic evaluation of existing historical convection-permitting climate simulations over the TP is an urgent requirement for reliable future climate change assessments

Research on Energy-Environment-Economy-Ecology Coupling Development in the Yellow River Basin

The Yellow River Basin is a major economic development area in China, and the high quality development of the basin still has great room for improvement. The coordinated development of energy, environment, economy and ecology is one of the keys to high quality development of the watershed. Aiming at the problem of energy, environment, economy and ecology restricting and promoting, this paper takes the nine provinces of the Yellow River as the research object, based on data from 2004 to 2017, through establishing the coupling coordination system of ecology, environment, economy and energy, using CRITIC method to study the characteristics of each subsystem of the basin in time and space. The results show that the score of energy, environment, economy and ecology is phased in time, and the level of compound coupling coordination is increasing year by year and the growth rate is obvious. The coupling coordination degree of the Yellow River basin has significant stage and regional characteristics in time and space, the coordination degree of the upper and middle reaches is higher than the coordination degree of the downstream coupling, but the difference between the three is gradually reduced with time

Research on the Coupling of Energy Consumption and High-quality Development in the Yellow River Basin

Understanding the energy development ideas of the Yellow River Basin has a very important impact on the high-quality development of the Yellow River Basin. Aiming at the restriction and promotion of energy consumption in the Yellow River Basin to its high-quality development, this article takes the nine provinces of the Yellow River as the research object, builds an evaluation model based on the data of the river basin from 2004 to 2017, and uses the entropy method and elastic coefficient method to study the coupling characteristics of watershed energy consumption for its high-quality development in time and space. The results show that the high-quality development level of the river basin is generally increasing, the consumption level is generally decreasing, and the coupling of energy consumption and high-quality development is optimized year by year. Finally, according to the coupling of energy consumption level and high-quality development level, a development strategy suitable for improvement is proposed

Deep Learning-Based Robust Visible Light Positioning for High-Speed Vehicles

Robustness is a key factor for real-time positioning and navigation, especially for high-speed vehicles. While visible light positioning (VLP) based on LED illumination and image sensors is widely studied, most of the VLP systems still suffer from the high positioning latency and the image blurs caused by high-speed movements. In this paper, a robust VLP system for high-speed vehicles is proposed based on a deep learning and data-driven approach. The proposed system can significantly increase the success rate of decoding VLP-LED user identifications (UID) from blurred images and reduce the computational latency for detecting and extracting VLP-LED stripe image regions from captured images. Experimental results show that the success rate of UID decoding using the proposed BN-CNN model could be higher than 98% when that of the traditional Zbar-based decoder falls to 0, while the computational time for positioning is decreased to 9.19 ms and the supported moving speed of our scheme can achieve 38.5 km/h. Therefore, the proposed VLP system can enhance the robustness against high-speed movement and guarantee the real-time response for positioning and navigation for high-speed vehicles