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

Characterization of a Ag+-Selective Electrode Based on Naphthalimide Derivative as Ionophore

A naphthalimide derivative has been explored as neutral ionophore for Ag+-selective electrode. Potentiometric response revealed that electrode based on the proposed ionophore with 2-nitrophenyl octyl ether as solvent in a poly (vinyl chloride) membrane matrix shows a measuring range of 1.0×10-6-1.0×10-2 M with a slope of 50.4±0.3 mV/decade. This electrode has high selectivity to Ag+ with respect to alkaline, alkaline earth and other heavy metal ions

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

SoccerNet 2023 Challenges Results

peer reviewedThe SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leaderboards are available on https://www.soccer-net.org. Baselines and development kits can be found on https://github.com/SoccerNet

TOA-Based Source Localization: A Linearization Approach Adopting Coordinate System Translation

This paper addresses the localization of a timing signal source based on the time of arrival (TOA) measurements that are collected from nearby sensors that are position known and synchronized to each other. Generally speaking, for such TOA-based source localization, the corresponding observation equations contain nonlinear relationship between measurements and unknown parameters, which normally results in the nonexistence of any efficient unbiased estimator that attains the Cramer-Rao lower bound (CRLB). In this paper, we devise a new approach that utilizes linearization and adopts suitable coordinate system translation to eliminate nonlinearity from the converted observation equations. The performance analysis and simulation study conducted show that our proposed algorithm can achieve the CRLB when the zero-mean Gaussian and independent measurement errors are sufficiently small

Incorporation of Amphipathic Diblock Copolymer in Lipid Bilayer for Improving pH Responsiveness

Diblock copolymers (mPEG-b-PDPA), which were designed to possess pH-sensitivity as well as amphipathy, were used as an intelligent lock in the liposomal membrane. The so-called pH-sensitive liposomes were prepared by simple mixing of the synthesized mPEG-b-PDPA with phospholipids and cholesterol. Fluorescence polarization at pH 7.4 showed that the membrane stability of the hybrid liposome was significantly increased compared with the pure liposome. Therefore, in the neutral environment, the leakage of doxorubicin (DOX) was inhibited. However, when pH decreased to 6.0, DOX release rate increased by 60% due to the escape of copolymer. The effects of the membrane composition and the PDPA segment length on bilayer membrane functions were investigated. These results revealed that the synthesized copolymers increased the difference in DOX cumulative release between pH 7.4 and 6.0, that is, improved the pH-controllability of the drug release from hybrid liposomes

Functionalized dithiocarbamate chelating resin for the removal of Co2+ from simulated wastewater

Abstract Industrial wastewater that contains trace amounts of heavy metal ions is often seen in petrochemical industry. While this wastewater can not be directly discharged, it is difficult to treat due to the low concentration of metal ions. Introducing chelating reagents into this wastewater for selective ion adsorption, followed by a mechanical separation process, provides an appealing solution. Toward the success of this technology, the development of effective chelating resins is of key importance. In the present work, a chelating resin containing amino and dithiocarbamate groups was reported for the removal of Co(II) metal ions in trace concentrations from simulated wastewater. By investigating the adsorption performance of the chelating resin at different solution pH values, adsorbent dosages, contact time, initial ion concentrations, and adsorption temperatures, the maximum adsorption capacity of the resin for Co(II) was identified to be 24.89 mg g−1 for a 2 g L−1 adsorbent dosage and a pH value of 5. After four adsorption–desorption cycles, 97% of the adsorption capacity of the resin was maintained. The adsorption kinetics and thermodynamics were analyzed and discussed as well