DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain

With the wide application of face recognition systems, there is rising concern that original face images could be exposed to malicious intents and consequently cause personal privacy breaches. This paper presents DuetFace, a novel privacy-preserving face recognition method that employs collaborative inference in the frequency domain. Starting from a counterintuitive discovery that face recognition can achieve surprisingly good performance with only visually indistinguishable high-frequency channels, this method designs a credible split of frequency channels by their cruciality for visualization and operates the server-side model on non-crucial channels. However, the model degrades in its attention to facial features due to the missing visual information. To compensate, the method introduces a plug-in interactive block to allow attention transfer from the client-side by producing a feature mask. The mask is further refined by deriving and overlaying a facial region of interest (ROI). Extensive experiments on multiple datasets validate the effectiveness of the proposed method in protecting face images from undesired visual inspection, reconstruction, and identification while maintaining high task availability and performance. Results show that the proposed method achieves a comparable recognition accuracy and computation cost to the unprotected ArcFace and outperforms the state-of-the-art privacy-preserving methods. The source code is available at https://github.com/Tencent/TFace/tree/master/recognition/tasks/duetface.Comment: Accepted to ACM Multimedia 202

Privacy-Preserving Face Recognition Using Random Frequency Components

The ubiquitous use of face recognition has sparked increasing privacy concerns, as unauthorized access to sensitive face images could compromise the information of individuals. This paper presents an in-depth study of the privacy protection of face images' visual information and against recovery. Drawing on the perceptual disparity between humans and models, we propose to conceal visual information by pruning human-perceivable low-frequency components. For impeding recovery, we first elucidate the seeming paradox between reducing model-exploitable information and retaining high recognition accuracy. Based on recent theoretical insights and our observation on model attention, we propose a solution to the dilemma, by advocating for the training and inference of recognition models on randomly selected frequency components. We distill our findings into a novel privacy-preserving face recognition method, PartialFace. Extensive experiments demonstrate that PartialFace effectively balances privacy protection goals and recognition accuracy. Code is available at: https://github.com/Tencent/TFace.Comment: ICCV 202

Privacy-Preserving Face Recognition with Learnable Privacy Budgets in Frequency Domain

Face recognition technology has been used in many fields due to its high recognition accuracy, including the face unlocking of mobile devices, community access control systems, and city surveillance. As the current high accuracy is guaranteed by very deep network structures, facial images often need to be transmitted to third-party servers with high computational power for inference. However, facial images visually reveal the user's identity information. In this process, both untrusted service providers and malicious users can significantly increase the risk of a personal privacy breach. Current privacy-preserving approaches to face recognition are often accompanied by many side effects, such as a significant increase in inference time or a noticeable decrease in recognition accuracy. This paper proposes a privacy-preserving face recognition method using differential privacy in the frequency domain. Due to the utilization of differential privacy, it offers a guarantee of privacy in theory. Meanwhile, the loss of accuracy is very slight. This method first converts the original image to the frequency domain and removes the direct component termed DC. Then a privacy budget allocation method can be learned based on the loss of the back-end face recognition network within the differential privacy framework. Finally, it adds the corresponding noise to the frequency domain features. Our method performs very well with several classical face recognition test sets according to the extensive experiments.Comment: ECCV 2022; Code is available at https://github.com/Tencent/TFace/tree/master/recognition/tasks/dctd

Developing a class of dual atom materials for multifunctional catalytic reactions

Dual atom catalysts, bridging single atom and metal/alloy nanoparticle catalysts, offer more opportunities to enhance the kinetics and multifunctional performance of oxygen reduction/evolution and hydrogen evolution reactions. However, the rational design of efficient multifunctional dual atom catalysts remains a blind area and is challenging. In this study, we achieved controllable regulation from Co nanoparticles to CoN4 single atoms to Co2N5 dual atoms using an atomization and sintering strategy via an N-stripping and thermal-migrating process. More importantly, this strategy could be extended to the fabrication of 22 distinct dual atom catalysts. In particular, the Co2N5 dual atom with tailored spin states could achieve ideally balanced adsorption/desorption of intermediates, thus realizing superior multifunctional activity. In addition, it endows Zn-air batteries with long-term stability for 800 h, allows water splitting to continuously operate for 1000 h, and can enable solar-powered water splitting systems with uninterrupted large-scale hydrogen production throughout day and night. This universal and scalable strategy provides opportunities for the controlled design of efficient multifunctional dual atom catalysts in energy conversion technologies

Impacts of the Wave-Dependent Sea Spray Parameterizations on Air–Sea–Wave Coupled Modeling under an Idealized Tropical Cyclone

While sea spray can significantly impact air–sea heat fluxes, the effect of spray produced by the interaction of wind and waves is not explicitly addressed in current operational numerical models. In the present work, the thermal effects of the sea spray were investigated for an idealized tropical cyclone (TC) through the implementation of different sea spray models into a coupled air–sea–wave numerical system. Wave-Reynolds-dependent and wave-steepness-dependent sea spray models were applied to test the sensitivity of local wind, wave, and ocean fields of this TC system. Results show that while the sensible heat fluxes decreased by up to 231 W m−2 (364%) and 159 W m−2 (251%), the latent heat fluxes increased by up to 359 W m−2 (89%) and 263 W m−2 (76%) in the simulation period, respectively. This results in an increase of the total heat fluxes by up to 135 W m−2 (32%) and 123 W m−2 (30%), respectively. Based on different sea spray models, sea spray decreases the minimum sea level pressure by up to 7 hPa (0.7%) and 8 hPa (0.8%), the maximum wind speed increases by up to 6.1 m s−1 (20%) and 5.7 m s−1 (19%), the maximum significant wave height increases by up to 1.1 m (17%) and 1.6 m (25%), and the minimum sea surface temperature decreases by up to 0.2 °C (0.8%) and 0.15 °C (0.6%), respectively. As the spray has such significant impacts on atmospheric and oceanic environments, it needs to be included in TC forecasting models

Cellular image classification

This book introduces new techniques for cellular image feature extraction, pattern recognition and classification. The authors use the antinuclear antibodies (ANAs) in patient serum as the subjects and the Indirect Immunofluorescence (IIF) technique as the imaging protocol to illustrate the applications of the described methods. Throughout the book, the authors provide evaluations for the proposed methods on two publicly available human epithelial (HEp-2) cell datasets: ICPR2012 dataset from the ICPR'12 HEp-2 cell classification contest and ICIP2013 training dataset from the ICIP'13 Competition on cells classification by fluorescent image analysis. First, the reading of imaging results is significantly influenced by one’s qualification and reading systems, causing high intra- and inter-laboratory variance. The authors present a low-order LP21 fiber mode for optical single cell manipulation and imaging staining patterns of HEp-2 cells. A focused four-lobed mode distribution is stable and effective in optical tweezer applications, including selective cell pick-up, pairing, grouping or separation, as well as rotation of cell dimers and clusters. Both translational dragging force and rotational torque in the experiments are in good accordance with the theoretical model. With a simple all-fiber configuration, and low peak irradiation to targeted cells, instrumentation of this optical chuck technology will provide a powerful tool in the ANA-IIF laboratories. Chapters focus on the optical, mechanical and computing systems for the clinical trials. Computer programs for GUI and control of the optical tweezers are also discussed. to more discriminative local distance vector by searching for local neighbors of the local feature in the class-specific manifolds. Encoding and pooling the local distance vectors leads to salient image representation. Combined with the traditional coding methods, this method achieves higher classification accuracy. Then, a rotation invariant textural feature of Pairwise Local Ternary Patterns with Spatial Rotation Invariant (PLTP-SRI) is examined. It is invariant to image rotations, meanwhile it is robust to noise and weak illumination. By adding spatial pyramid structure, this method captures spatial layout information. While the proposed PLTP-SRI feature extracts local feature, the BoW framework builds a global image representation. It is reasonable to combine them together to achieve impressive classification performance, as the combined feature takes the advantages of the two kinds of features in different aspects. Finally, the authors design a Co-occurrence Differential Texton (CoDT) feature to represent the local image patches of HEp-2 cells. The CoDT feature reduces the information loss by ignoring the quantization while it utilizes the spatial relations among the differential micro-texton feature. Thus it can increase the discriminative power. A generative model adaptively characterizes the CoDT feature space of the training data. Furthermore, exploiting a discriminant representation allows for HEp-2 cell images based on the adaptive partitioned feature space. Therefore, the resulting representation is adapted to the classification task. By cooperating with linear Support Vector Machine (SVM) classifier, this framework can exploit the advantages of both generative and discriminative approaches for cellular image classification. The book is written for those researchers who would like to develop their own programs, and the working MatLab codes are included for all the important algorithms presented. It can also be used as a reference book for graduate students and senior undergraduates in the area of biomedical imaging, image feature extraction, pattern recognition and classification. Academics, researchers, and professional will find this to be an exceptional resource

A Wind–Wave-Dependent Sea Spray Volume Flux Model Based on Field Experiments

Sea spray can contribute significantly to the exchanges of heat and momentum across the air–sea interface. However, while critical, sea spray physics are typically not included in operational atmospheric and oceanic models due to large uncertainties in their parameterizations. In large part, this is because of the scarcity of in-situ sea spray observations which prevent rigorous validation of existing sea spray models. Moreover, while sea spray is critically produced through the fundamental interactions between wind and waves, traditionally, sea spray models are parameterized in terms of wind properties only. In this study, we present novel in-situ observations of sea spray derived from a laser altimeter through the adoption of the Beer–Lambert law. Observations of sea spray cover a broad range of wind and wave properties and are used to develop a wind–wave-dependent sea spray volume flux model. Improved performance of the model is observed when wave properties are included, in contrast to a parameterization based on wind properties alone. The novel in-situ sea spray observations and the predictive model derived here are consistent with the classic spray model in both trend and magnitude. Our model and novel observations provide opportunities to improve the prediction of air–sea fluxes in operational weather forecasting models

Induced dopaminergic neurons: A new promise for Parkinson’s disease

Motor symptoms that define Parkinson’s disease (PD) are caused by the selective loss of nigral dopaminergic (DA) neurons. Cell replacement therapy for PD has been focused on midbrain DA neurons derived from human fetal mesencephalic tissue, human embryonic stem cells (hESC) or human induced pluripotent stem cells (iPSC). Recent development in the direct conversion of human fibroblasts to induced dopaminergic (iDA) neurons offers new opportunities for transplantation study and disease modeling in PD. The iDA neurons are generated directly from human fibroblasts in a short period of time, bypassing lengthy differentiation process from human pluripotent stem cells and the concern for potentially tumorigenic mitotic cells. They exhibit functional dopaminergic neurotransmission and relieve locomotor symptoms in animal models of Parkinson’s disease. In this review, we will discuss this recent development and its implications to Parkinson’s disease research and therapy. Keywords: Parkinson’s disease, Induced dopaminergic neuron, Induced neuron, Induced pluripotent stem cell, Transcription facto