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

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

The m6A Reader IGF2BP2 Regulates Macrophage Phenotypic Activation and Inflammatory Diseases by Stabilizing TSC1 and PPARγ.

peer reviewedPhenotypic polarization of macrophages is regulated by a milieu of cues in the local tissue microenvironment. Currently, little is known about how the intrinsic regulators modulate proinflammatory (M1) versus prohealing (M2) macrophages activation. Here, it is observed that insulin-like growth factor 2 messenger RNA (mRNA)-binding protein 2 (IGF2BP2)-deleted macrophages exhibit enhanced M1 phenotype and promote dextran sulfate sodium induced colitis development. However, the IGF2BP2-/- macrophages are refractory to interleukin-4 (IL-4) induced activation and alleviate cockroach extract induced pulmonary allergic inflammation. Molecular studies indicate that IGF2BP2 switches M1 macrophages to M2 activation by targeting tuberous sclerosis 1 via an N6-methyladenosine (m6A)-dependent manner. Additionally, it is also shown a signal transducer and activators of transcription 6 (STAT6)-high mobility group AT-hook 2-IGF2BP2-peroxisome proliferator activated receptor-γ axis involves in M2 macrophages differentiation. These findings highlight a key role of IGF2BP2 in regulation of macrophages activation and imply a potential therapeutic target of macrophages in the inflammatory diseases

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas

Golden single-atomic-site platinum electrocatalysts

Bimetallic nanoparticles with tailored structures constitute a desirable model system for catalysts, as crucial factors such as geometric and electronic effects can be readily controlled by tailoring the structure and alloy bonding of the catalytic site. Here we report a facile colloidal method to prepare a series of platinum–gold (PtAu) nanoparticles with tailored surface structures and particle diameters on the order of 7 nm. Samples with low Pt content, particularly Pt 4 Au 96 , exhibited unprecedented electrocatalytic activity for the oxidation of formic acid. A high forward current density of 3.77 A mg Pt −1 was observed for Pt 4 Au 96 , a value two orders of magnitude greater than those observed for core–shell structured Pt 78 Au 22 and a commercial Pt nanocatalyst. Extensive structural characterization and theoretical density functional theory simulations of the best-performing catalysts revealed densely packed single-atom Pt surface sites surrounded by Au atoms, which suggests that their superior catalytic activity and selectivity could be attributed to the unique structural and alloy-bonding properties of these single-atomic-site catalysts