随着智能传感技术在如卫生间和更衣室等敏感环境中日益广泛的应用，视觉监控系统面临着深刻的隐私-安全悖论。现有的隐私保护方法，包括物理脱敏、加密和模糊处理，往往会损害语义理解或无法确保数学上可证明的不可逆性。尽管Privacy Camera 1.0在源头上消除了视觉数据以防止泄露，但它只提供了文本判断，导致在争议中存在证据盲区。为了解决这些限制，本文提出了一种基于AI流范式和协作边缘-云架构的新型隐私保护感知框架。通过在边缘部署视觉脱敏器，原始图像通过非线性映射和随机噪声注入，在信息瓶颈原理下实时转换为抽象特征向量，确保身份敏感信息被剥离，原始图像在数学上无法重建。抽象表示通过
英文摘要：
With the increasing deployment of intelligent sensing technologies in highly sensitive environments such as restrooms and locker rooms, visual surveillance systems face a profound privacy-security paradox. Existing privacy-preserving approaches, including physical desensitization, encryption, and obfuscation, often compromise semantic understanding or fail to ensure mathematically provable irreversibility. Although Privacy Camera 1.0 eliminated visual data at the source to prevent leakage, it provided only textual judgments, leading to evidentiary blind spots in disputes. To address these limitations, this paper proposes a novel privacy-preserving perception framework based on the AI Flow paradigm and a collaborative edge-cloud architecture. By deploying a visual desensitizer at the edge, raw images are transformed in real time into abstract feature vectors through nonlinear mapping and stochastic noise injection under the Information Bottleneck principle, ensuring identity-sensitive information is stripped and original images are mathematically unreconstructable. The abstract representations are transmitted to the cloud for behavior recognition and semantic reconstruction via a "dynamic contour" visual language, achieving a critical balance between perception and privacy while enabling illustrative visual reference without exposing raw images.
Stony Brook University（石溪大学）；Temple University（天普大学）；University of South Carolina（南卡罗来纳大学）
Many critical applications rely on cameras to capture video footage for analytical purposes. This has led to concerns about these cameras accidentally capturing more information than is necessary. In this paper, we propose a deep learning approach towards protecting privacy in camera-based systems. Instead of specifying specific objects (e.g. faces) are privacy sensitive, our technique distinguishes between salient (visually prominent) and non-salient objects based on the intuition that the latter is unlikely to be needed by the application.
Hong Kong University of Science and Technology（香港科技大学）
The growing popularity of mobile and wearable devices with built-in cameras, the bright prospect of camera related applications such as augmented reality and life-logging system, the increased ease of taking and sharing photos, and advances in computer vision techniques have greatly facilitated people's lives in many aspects, but have also inevitably raised people's concerns about visual privacy at the same time. Motivated by recent user studies that people's privacy concerns are dependent on the context, in this paper, we propose Cardea, a context-aware and interactive visual privacy protecti…
In this work, we present CCTV-Exposure -- the first CCTV-aware solution to evaluate potential privacy exposure to closed-circuit television (CCTV) cameras. The objective was to develop a toolset for quantifying human exposure to CCTV cameras from a privacy perspective. Our novel approach is trajectory analysis of the individuals, coupled with a database of geo-location mapped CCTV cameras annotated with minimal yet sufficient meta-information. For this purpose, CCTV-Exposure model based on a Global Positioning System (GPS) tracking was applied to estimate individual privacy exposure in differe…
In spite of the legal advances in personal data protection, the issue of private data being misused by unauthorized entities is still of utmost importance. To prevent this, Privacy by Design is often proposed as a solution for data protection. In this paper, the effect of camera distortions is studied using Deep Learning techniques commonly used to extract sensitive data. To do so, we simulate out-of-focus images corresponding to a realistic conventional camera with fixed focal length, aperture, and focus, as well as grayscale images coming from a monochrome camera. We then prove, through an e…
In order to withstand the ever-increasing invasion of privacy by CCTV cameras and technologies, on par CCTV-aware solutions must exist that provide privacy, safety, and cybersecurity features. We argue that a first important step towards such CCTV-aware solutions must be a mapping system (e.g., Google Maps, OpenStreetMap) that provides both privacy and safety routing and navigation options. However, this in turn requires that the mapping system contains updated information on CCTV cameras' exact geo-location, coverage area, and possibly other meta-data (e.g., resolution, facial recognition fea…
In this work, we use a ceiling-mounted omni-directional camera to detect people in a room. This can be used as a sensor to measure the occupancy of meeting rooms and count the amount of flex-desk working spaces available. If these devices can be integrated in an embedded low-power sensor, it would form an ideal extension of automated room reservation systems in office environments. The main challenge we target here is ensuring the privacy of the people filmed. The approach we propose is going to extremely low image resolutions, such that it is impossible to recognise people or read potentially…
Pose estimation is an important technique for nonverbal human-robot interaction. That said, the presence of a camera in a person's space raises privacy concerns and could lead to distrust of the robot. In this paper, we propose a privacy-preserving camera-based pose estimation method. The proposed system consists of a user-controlled translucent filter that covers the camera and an image enhancement module designed to facilitate pose estimation from the filtered (shadow) images, while never capturing clear images of the user. We evaluate the system's performance on a new filtered image dataset…
EgoPrivacy: What Your First-Person Camera Says About You?
University of California, San Diego（加州大学圣地亚哥分校）；Qualcomm AI Research, an initiative of Qualcomm Technologies, Inc（高通人工智能研究）；University of Electronic Science and Technology of China（电子科技大学）
The University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
This article presents REVAMP$^2$T, Real-time Edge Video Analytics for Multi-camera Privacy-aware Pedestrian Tracking, as an integrated end-to-end IoT system for privacy-built-in decentralized situational awareness. REVAMP$^2$T presents novel algorithmic and system constructs to push deep learning and video analytics next to IoT devices (i.e. video cameras). On the algorithm side, REVAMP$^2$T proposes a unified integrated computer vision pipeline for detection, re-identification, and tracking across multiple cameras without the need for storing the streaming data. At the same time, it avoids fa…
Privacy-Preserving Representations are not Enough -- Recovering Scene Content from Camera Poses
Chalmers University of Technology（查尔姆斯理工大学）；Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague（捷克布拉格技术大学捷克信息学、机器人学与控制论研究所）；Faculty of Electrical Engineering, Czech Technical University in Prague（捷克布拉格技术大学电气工程学院）
Image-based localization is a core component of many augmented/mixed reality (AR/MR) and autonomous robotic systems. Current localization systems rely on the persistent storage of 3D point clouds of the scene to enable camera pose estimation, but such data reveals potentially sensitive scene information. This gives rise to significant privacy risks, especially as for many applications 3D mapping is a background process that the user might not be fully aware of. We pose the following question: How can we avoid disclosing confidential information about the captured 3D scene, and yet allow reliab…
Video surveillance has become ubiquitous in the modern world. Mobile devices, surveillance cameras, and IoT devices, all can record video that can violate our privacy. One proposed solution for this is privacy-preserving video, which removes identifying information from the video as it is produced. Several algorithms for this have been proposed, but all of them suffer from scale issues: in order to sufficiently anonymize near-camera objects, distant objects become unidentifiable. In this paper, we propose a scale-invariant method, based on wavelet decomposition.
Laboratoire d’imagerie, de vision et d’intelligence artificielle (LIVIA)（成像、视觉与人工智能实验室（LIVIA））；ETS Montreal（蒙特利尔高等技术学院）
In intelligent building management, knowing the number of people and their location in a room are important for better control of its illumination, ventilation, and heating with reduced costs and improved comfort. This is typically achieved by detecting people using compact embedded devices that are installed on the room's ceiling, and that integrate low-resolution infrared camera, which conceals each person's identity. However, for accurate detection, state-of-the-art deep learning models still require supervised training using a large annotated dataset of images. In this paper, we investigat…
École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
By replacing the lens with a thin optical element, lensless imaging enables new applications and solutions beyond those supported by traditional camera design and post-processing, e.g. compact and lightweight form factors and visual privacy. The latter arises from the highly multiplexed measurements of lensless cameras, which require knowledge of the imaging system to recover a recognizable image. In this work, we exploit this unique multiplexing property: casting the optics as an encoder that produces learned embeddings directly at the camera sensor. We do so in the context of image classific…
Massachusetts Institute of Technology（麻省理工学院）；Sony AI（索尼人工智能研究院）
Harms resulting from the development and deployment of facial processing technologies (FPT) have been met with increasing controversy. Several states and cities in the U.S. have banned the use of facial recognition by law enforcement and governments, but FPT are still being developed and used in a wide variety of contexts where they primarily are regulated by state biometric information privacy laws. Among these laws, the 2008 Illinois Biometric Information Privacy Act (BIPA) has generated a significant amount of litigation. Yet, with most BIPA lawsuits reaching settlements before there have b…
National Institute of Advanced Industrial Science and Technology (AIST)（产业技术综合研究所）；Tokyo Institute of Technology（东京工业大学）
This study proposes a privacy-preserving Visual SLAM framework for estimating camera poses and performing bundle adjustment with mixed line and point clouds in real time. Previous studies have proposed localization methods to estimate a camera pose using a line-cloud map for a single image or a reconstructed point cloud. These methods offer a scene privacy protection against the inversion attacks by converting a point cloud to a line cloud, which reconstruct the scene images from the point cloud. However, they are not directly applicable to a video sequence because they do not address computat…
Northwestern University（西北大学）；Microsoft Research（微软研究院）；University of Florida（佛罗里达大学）；Zillow Group（Zillow集团）
The risk of unauthorized remote access of streaming video from networked cameras underlines the need for stronger privacy safeguards. We propose a lens-free coded aperture camera system for human action recognition that is privacy-preserving. While coded aperture systems exist, we believe ours is the first system designed for action recognition without the need for image restoration as an intermediate step. Action recognition is done using a deep network that takes in as input, non-invertible motion features between pairs of frames computed using phase correlation and log-polar transformation.…
Heterogeneous Model Fusion for Privacy-Aware Multi-Camera Surveillance via Synthetic Domain Adaptation
Department of Computer Science and Information Engineering, National Chung Cheng University（资讯工程系，国立 Chung Cheng 大学）；Department of Computer Science, National Yang Ming Chiao Tung University（计算机科学系，国立阳明交通大学）；National Center for High-Performance Computing（国家高速计算中心）
For the last several decades, the increased, widespread, unwarranted, and unaccountable use of Closed-Circuit TeleVision (CCTV) cameras globally has raised concerns about privacy risks. Additional recent features of many CCTV cameras, such as Internet of Things (IoT) connectivity and Artificial Intelligence (AI)-based facial recognition, only increase concerns among privacy advocates. Therefore, on par \emph{CCTV-aware solutions} must exist that provide privacy, safety, and cybersecurity features. We argue that an important step forward is to develop solutions addressing privacy concerns via r…
We introduce a framework for navigating through cluttered environments by connecting multiple cameras together while simultaneously preserving privacy. Occlusions and obstacles in large environments are often challenging situations for navigation agents because the environment is not fully observable from a single camera view. Given multiple camera views of an environment, our approach learns to produce a multiview scene representation that can only be used for navigation, provably preventing one party from inferring anything beyond the output task. On a new navigation dataset that we will pub…
In action understanding in indoor, we have to recognize human pose and action considering privacy. Although camera images can be used for highly accurate human action recognition, camera images do not preserve privacy. Therefore, we propose a new task for human instance segmentation from invisible information, especially airborne ultrasound, for action recognition. To perform instance segmentation from invisible information, we first convert sound waves to reflected sound directional images (sound images). Although the sound images can roughly identify the location of a person, the detailed sh…
Evaluation Protocols and Validation for Cameras in Indoor Healthcare Monitoring
1 School of Engineering Mathematics；Technology, University of Bristol, UK 2 School of Computer Science, University of Bristol, UK 3 Translational；Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, UK 4 NIHR Newcastle Biomedical Research Centre, Newcastle University；The Newcastle upon Tyne Hospitals NHS Foundation Trust, UK
École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）
Lensless imaging can provide visual privacy due to the highly multiplexed characteristic of its measurements. However, this alone is a weak form of security, as various adversarial attacks can be designed to invert the one-to-many scene mapping of such cameras. In this work, we enhance the privacy provided by lensless imaging by (1) downsampling at the sensor and (2) using a programmable mask with variable patterns as our optical encoder. We build a prototype from a low-cost LCD and Raspberry Pi components, for a total cost of around 100 USD. This very low price point allows our system to be d…
CityGuard: Graph-Aware Private Descriptors for Bias-Resilient Identity Search Across Urban Cameras
University of Macau（澳门大学）；Tsinghua University（清华大学）；Renmin University of China（中国人民大学）；Shanghai AI Laboratory（上海人工智能实验室）；The Hong Kong University of Science and Technology（香港科技大学）；Juntendo University（顺天堂大学）；Peking University（北京大学）
Imperial College London（伦敦帝国理工学院）；University of Pittsburgh（匹兹堡大学）；Baylor College Of Medicine（贝勒医学院）；University of Ghana（加纳大学）；University of Georgia（佐治亚大学）；Boston University（波士顿大学）
Camera-based passive dietary intake monitoring is able to continuously capture the eating episodes of a subject, recording rich visual information, such as the type and volume of food being consumed, as well as the eating behaviours of the subject. However, there currently is no method that is able to incorporate these visual clues and provide a comprehensive context of dietary intake from passive recording (e.g., is the subject sharing food with others, what food the subject is eating, and how much food is left in the bowl). On the other hand, privacy is a major concern while egocentric weara…
Bullying10K: A Large-Scale Neuromorphic Dataset towards Privacy-Preserving Bullying Recognition
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Center for Long-term Artificial Intelligence（长期人工智能研究中心）
Privacy-Preserving Structureless Visual Localization via Image Obfuscation
Faculty of Electrical Engineering, Czech Technical University (CTU) in Prague（捷克技术大学（CTU）布拉格电气工程学院）；Czech Institute of Informatics, Robotics and Cybernetics, CTU in Prague（捷克信息学、机器人学与自动化研究院，CTU布拉格）；Visual Recognition Group, Faculty of Electrical Engineering, CTU in Prague（视觉识别组，CTU布拉格电气工程学院）
Vienna University of Technology（维也纳技术大学）；University of Alicante（阿利坎特大学）
As the privacy risks posed by camera surveillance and facial recognition have grown, so has the research into privacy preservation algorithms. Among these, visual privacy preservation algorithms attempt to impart bodily privacy to subjects in visuals by obfuscating privacy-sensitive areas. While disparate performances of facial recognition systems across phenotypes are the subject of much study, its counterpart, privacy preservation, is not commonly analysed from a fairness perspective. In this paper, the fairness of commonly used visual privacy preservation algorithms is investigated through …
California Institute of Technology（加州理工学院）；Harvard University（哈佛大学）
To protect the location of camera trap data containing sensitive, high-target species, many ecologists randomly obfuscate the latitude and longitude of the camera when publishing their data. For example, they may publish a random location within a 1km radius of the true camera location for each camera in their network. In this paper, we investigate the robustness of geo-obfuscation for maintaining camera trap location privacy, and show via a case study that a few simple, intuitive heuristics and publicly available satellite rasters can be used to reduce the area likely to contain the camera by…
Lens Privacy Sealing: A New Benchmark and Method for Physical Privacy-Preserving Action Recognition
State Key Laboratory of General Artificial Intelligence, Peking University, Shenzhen Graduate School（北京大学深圳研究生院通用人工智能国家重点实验室）；Department of Computer Science and Engineering, State University of New York at Buffalo（纽约州立大学布法罗分校计算机科学与工程系）
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
Recent advancements in artificial intelligence (AI) have seen the emergence of smart video surveillance (SVS) in many practical applications, particularly for building safer and more secure communities in our urban environments. Cognitive tasks, such as identifying objects, recognizing actions, and detecting anomalous behaviors, can produce data capable of providing valuable insights to the community through statistical and analytical tools. However, artificially intelligent surveillance systems design requires special considerations for ethical challenges and concerns. The use and storage of …
Privacy-Preserving in Connected and Autonomous Vehicles Through Vision to Text Transformation
organization= Department of Computre Science, Texas A\&M University Corpus Christi , addressline= 6300 Ocean Dr , city= Corpus Christi , postcode= 78412 , state= Texas , country= USA；organization= Department of Information Management, International Graduate School of Artificial Intelligence, National Yunlin University of Science；organization= Institute of the Information Society, Ludovika University of Public Service , city= Budapest , postcode= 78412 , country= Hungary；organization= North Carolina A\&T State University , addressline= 601 E Market , city= Greensboro , postcode= 27411 , country= USA
We experimentally demonstrate a camera whose primary optic is a cannula (diameter=0.22mm and length=12.5mm) that acts a lightpipe transporting light intensity from an object plane (35cm away) to its opposite end. Deep neural networks (DNNs) are used to reconstruct color and grayscale images with field of view of 180 and angular resolution of ~0.40. When trained on images with depth information, the DNN can create depth maps. Finally, we show DNN-based classification of the EMNIST dataset without and with image reconstructions. The former could be useful for imaging with enhanced privacy.
University of California, Los Angeles（加州大学洛杉矶分校）；California NanoSystems Institute (CNSI)（加州纳米系统研究所（CNSI））
Privacy protection is a growing concern in the digital era, with machine vision techniques widely used throughout public and private settings. Existing methods address this growing problem by, e.g., encrypting camera images or obscuring/blurring the imaged information through digital algorithms. Here, we demonstrate a camera design that performs class-specific imaging of target objects with instantaneous all-optical erasure of other classes of objects. This diffractive camera consists of transmissive surfaces structured using deep learning to perform selective imaging of target classes of obje…
