Data sharing for medical research has been difficult as open-sourcing clinical data may violate patient privacy. Traditional methods for face de-identification wipe out facial information entirely, making it impossible to analyze facial behavior. Recent advancements on whole-body keypoints detection also rely on facial input to estimate body keypoints. Both facial and body keypoints are critical in some medical diagnoses, and keypoints invariability after de-identification is of great importance. Here, we propose a solution using deepfake technology, the face swapping technique. While this swapping method has been criticized for invading privacy and portraiture right, it could conversely protect privacy in medical video: patients' faces could be swapped to a proper target face and become unrecognizable. However, it remained an open question that to what extent the swapping de-identification method could affect the automatic detection of body keypoints. In this study, we apply deepfake technology to Parkinson's disease examination videos to de-identify subjects, and quantitatively show that: face-swapping as a de-identification approach is reliable, and it keeps the keypoints almost invariant, significantly better than traditional methods. This study proposes a pipeline for video de-identification and keypoint preservation, clearing up some ethical restrictions for medical data sharing. This work could make open-source high quality medical video datasets more feasible and promote future medical research that benefits our society.
Waseda University（早稻田大学）；Keio University（庆应义塾大学）；AIST（产业技术综合研究所）；Waseda Research Institute for Science and Engineering（早稻田大学科学与工程研究院）
To protect privacy and prevent malicious use of deepfake, current studies propose methods that interfere with the generation process, such as detection and destruction approaches. However, these methods suffer from sub-optimal generalization performance to unseen models and add undesirable noise to the original image. To address these problems, we propose a new problem formulation for deepfake prevention: generating a ``scapegoat image'' by modifying the style of the original input in a way that is recognizable as an avatar by the user, but impossible to reconstruct the real face. Even in the …
PVLM: Parsing-Aware Vision Language Model with Dynamic Contrastive Learning for Zero-Shot Deepfake Attribution
Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences)（计算机科学与技术学院，齐鲁工业大学（山东省科学院））；Shandong University of Science and Technology（山东科技大学）；Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences)（山东省人工智能研究院，齐鲁工业大学（山东省科学院））；School of Electronics and Information Engineering, Harbin Institute of Technology（电子与信息工程学院，哈尔滨工业大学（深圳））；School of Computer Science and Technology, Shandong University（计算机科学与技术学院，山东大学）
This paper reviews the state of the art in visual privacy protection techniques, with particular attention paid to techniques applicable to the field of active and assisted living (AAL). A novel taxonomy with which state-of-the-art visual privacy protection methods can be classified is introduced. Perceptual obfuscation methods, a category in the taxonomy, is highlighted. These are a category of visual privacy preservation techniques particularly relevant when considering scenarios that come under video-based AAL monitoring. Obfuscation against machine learning models is also explored. A high-…
DeepFake face swapping presents a significant threat to online security and social media, which can replace the source face in an arbitrary photo/video with the target face of an entirely different person. In order to prevent this fraud, some researchers have begun to study the adversarial methods against DeepFake or face manipulation. However, existing works focus on the white-box setting or the black-box setting driven by abundant queries, which severely limits the practical application of these methods. To tackle this problem, we introduce a practical adversarial attack that does not requir…
Samsung SDS（三星SDS）；Seoul National University（首尔大学）；POSTECH（浦项工科大学）
In this work, we present Facial Identity Controllable GAN (FICGAN) for not only generating high-quality de-identified face images with ensured privacy protection, but also detailed controllability on attribute preservation for enhanced data utility. We tackle the less-explored yet desired functionality in face de-identification based on the two factors. First, we focus on the challenging issue to obtain a high level of privacy protection in the de-identification task while uncompromising the image quality. Second, we analyze the facial attributes related to identity and non-identity and explor…
Deepfake poses a serious threat to the reliability of judicial evidence and intellectual property protection. In spite of an urgent need for Deepfake identification, existing pixel-level detection methods are increasingly unable to resist the growing realism of fake videos and lack generalization. In this paper, we propose a scheme to expose Deepfake through faint signals hidden in face videos. This scheme extracts two types of minute information hidden between face pixels-photoplethysmography (PPG) features and auto-regressive (AR) features, which are used as the basis for forensics in the te…
Learning Spatiotemporal Inconsistency via Thumbnail Layout for Face Deepfake Detection
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of Science and Technology of China（中国科学技术大学）
A Survey on Facial Image Privacy Preservation in Cloud-Based Services
Nanyang Technological University（南洋理工大学）；School of Cyber Science and Engineering, Wuhan University（武汉大学网络安全学院）；College of Electrical Engineering, Zhejiang University（浙江大学电气工程学院）
TALL: Thumbnail Layout for Deepfake Video Detection
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, UCAS（中国科学院大学网络空间安全学院）；CRIPAC & MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别国家重点实验室与智能分析与计算研究所）；School of Artificial Intelligence, UCAS（中国科学院大学人工智能学院）；School of Communications and Information Engineering, NJUPT（南京邮电大学通信与信息工程学院）；OPPO Research Institute（OPPO研究院）
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 …
Seeing is not Believing: An Identity Hider for Human Vision Privacy Protection
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies（广东省新型安全智能技术重点实验室）；State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所信息安全国家重点实验室）；School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）
Who Can See Through You? Adversarial Shielding Against VLM-Based Attribute Inference Attacks
East China Normal University（东华大学）；Zhongguancun Academy（中关村学院）；Dept. of Comp. Sci. and Tech., Institute for AI, Tsinghua University（计算机科学与技术系，人工智能研究院，清华大学）
Disruptive Attacks on Face Swapping via Low-Frequency Perceptual Perturbations
Department of Mathematics and Artificial Intelligence（数学与人工智能系）；Shandong Artificial Intelligence Institute（山东省人工智能研究所）；Qilu University of Technology（齐鲁大学）
Cyprus University of Technology（塞浦路斯理工大学）；National Technical University of Athens（雅典国家技术大学）
Underwater Cultural Heritage (CH) sites are widely spread; from ruins in coastlines up to shipwrecks in deep. The documentation and preservation of this heritage is an obligation of the mankind, dictated also by the international treaties like the Convention on the Protection of the Underwater Cultural Her-itage which fosters the use of "non-destructive techniques and survey meth-ods in preference over the recovery of objects". However, submerged CH lacks in protection and monitoring in regards to the land CH and nowadays recording and documenting, for digital preservation as well as dissemina…
Imperceptible and Reversible Adversarial Examples against Vision-Language Models for Privacy Protection
School of Cyber Science and Engineering, Huazhong University of Science and Technology（华中科技大学网络空间安全学院）；College of Computer Science, Chongqing University（重庆大学计算机科学学院）；School of Software and engineering, Huazhong University of Science and Technology（华中科技大学软件工程学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
Security monitoring via ubiquitous cameras and their more extended in intelligent buildings stand to gain from advances in signal processing and machine learning. While these innovative and ground-breaking applications can be considered as a boon, at the same time they raise significant privacy concerns. In fact, recent GDPR (General Data Protection Regulation) legislation has highlighted and become an incentive for privacy-preserving solutions. Typical privacy-preserving video monitoring schemes address these concerns by either anonymizing the sensitive data. However, these approaches suffer …
Smart sensors, devices and systems deployed in smart cities have brought improved physical protections to their citizens. Enhanced crime prevention, and fire and life safety protection are achieved through these technologies that perform motion detection, threat and actors profiling, and real-time alerts. However, an important requirement in these increasingly prevalent deployments is the preservation of privacy and enforcement of protection of personal identifiable information. Thus, strong encryption and anonymization techniques should be applied to the collected data. In this IEEE Big Data …
GeoCloak: An Agentic Framework for Semantics-Preserving Geolocation Privacy via Peripheral Clue Manipulation
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；CISPA Helmholtz Center for Information Security（CISPA赫尔姆霍兹信息安全中心）
Face de-identification has become increasingly important as the image sources are explosively growing and easily accessible. The advance of new face recognition techniques also arises people's concern regarding the privacy leakage. The mainstream pipelines of face de-identification are mostly based on the k-same framework, which bears critiques of low effectiveness and poor visual quality. In this paper, we propose a new framework called Privacy-Protective-GAN (PP-GAN) that adapts GAN with novel verificator and regulator modules specially designed for the face de-identification problem to ensu…
Reliable facial expression recognition plays a critical role in human-machine interactions. However, most of the facial expression analysis methodologies proposed to date pay little or no attention to the protection of a user's privacy. In this paper, we propose a Privacy-Preserving Representation-Learning Variational Generative Adversarial Network (PPRL-VGAN) to learn an image representation that is explicitly disentangled from the identity information. At the same time, this representation is discriminative from the standpoint of facial expression recognition and generative as it allows expr…
From TV news to Google StreetView, face obscuration has been used for privacy protection. Due to recent advances in the field of deep learning, obscuration methods such as Gaussian blurring and pixelation are not guaranteed to conceal identity. In this paper, we propose a utility-preserving generative model, UP-GAN, that is able to provide an effective face obscuration, while preserving facial utility. By utility-preserving we mean preserving facial features that do not reveal identity, such as age, gender, skin tone, pose, and expression. We show that the proposed method achieves the best per…
