G2Face: High-Fidelity Reversible Face Anonymization via Generative and Geometric Priors
South China University of Technology（华南理工大学）；Guangdong Engineering Center for Large Model and GenAI Technology（广东省大模型与生成式人工智能技术工程中心）；State Key Laboratory of Subtropical Building and Urban Science（亚热带建筑与城市科学国家重点实验室）；The Hong Kong Polytechnic University（香港理工大学）
Haoxin Yang, Xuemiao Xu, Cheng Xu, Huaidong Zhang, Jing Qin, Yi Wang, Pheng-Ann Heng, Shengfeng He
Reversible face anonymization, unlike traditional face pixelization, seeks to replace sensitive identity information in facial images with synthesized alternatives, preserving privacy without sacrificing image clarity. Traditional methods, such as encoder-decoder networks, often result in significant loss of facial details due to their limited learning capacity. Additionally, relying on latent manipulation in pre-trained GANs can lead to changes in ID-irrelevant attributes, adversely affecting data utility due to GAN inversion inaccuracies. This paper introduces G\textsuperscript{2}Face, which leverages both generative and geometric priors to enhance identity manipulation, achieving high-quality reversible face anonymization without compromising data utility. We utilize a 3D face model to extract geometric information from the input face, integrating it with a pre-trained GAN-based decoder. This synergy of generative and geometric priors allows the decoder to produce realistic anonymized faces with consistent geometry. Moreover, multi-scale facial features are extracted from the original face and combined with the decoder using our novel identity-aware feature fusion blocks (IFF). This integration enables precise blending of the generated facial patterns with the original ID-irrelevant features, resulting in accurate identity manipulation. Extensive experiments demonstrate that our method outperforms existing state-of-the-art techniques in face anonymization and recovery, while preserving high data utility. Code is available at https://github.com/Harxis/G2Face.
Secure and reversible face anonymization with diffusion models
LIRMM, Univ Montpellier, CNRS, Montpellier, FRANCE（LIRMM，蒙彼利埃大学，法国国家科学研究中心，蒙彼利埃，法国）；IMT Nord Europe, Institut Mines-Télécom, Univ. Lille, Centre for Digital Systems, Lille, FRANCE（IMT北欧，法国电信研究院，里尔大学，数字系统研究中心，里尔，法国）
Anonymization of medical images is necessary for protecting the identity of the test subjects, and is therefore an essential step in data sharing. However, recent developments in deep learning may raise the bar on the amount of distortion that needs to be applied to guarantee anonymity. To test such possibilities, we have applied the novel CycleGAN unsupervised image-to-image translation framework on sagittal slices of T1 MR images, in order to reconstruct facial features from anonymized data. We applied the CycleGAN framework on both face-blurred and face-removed images. Our results show that…
Beyond Inference Intervention: Identity-Decoupled Diffusion for Face Anonymization
School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）；School of Future Technology, South China University of Technology（华南理工大学未来技术学院）；School of Computing and Information Systems, Singapore Management University（新加坡管理学院计算机与信息学院）
Faithful Extreme Image Rescaling with Learnable Reversible Transformation and Semantic Priors
State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（人机混合增强智能国家重点实验室，人工智能与机器人研究院，西安交通大学）；School of Information and Communications Engineering, Xi’an Jiaotong University（信息与通信工程学院，西安交通大学）；Department of Computer Science and Software Engineering, The University of Western Australia（计算机科学与软件工程系，西澳大学）
Stable and Near-Reversible Diffusion ODE Solvers for Image Editing
Department of Computing, Imperial College London, London, United Kingdom（帝国理工学院伦敦分校计算机系，伦敦，英国）；Department of Mathematics, Imperial College London, London, United Kingdom（帝国理工学院伦敦分校数学系，伦敦，英国）
EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical Data Sharing
UKRI CDT in AI for Healthcare, Imperial College London（英国帝国理工学院UKRI医疗保健人工智能博士培训中心）；Department of Computing, Imperial College London（帝国理工学院计算系）；Friedrich–Alexander University Erlangen–Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；School of BMEIS, King’s College London（伦敦国王学院BMEIS学院）；Guy’s and St Thomas’ NHS Foundation Trust（盖伊和圣托马斯NHS基金会信托）；Ultromics Ltd（Ultromics有限公司）；John Radcliffe Hospital, Cardiovascular Clinical Research Facility（约翰拉德克利夫医院心血管临床研究中心）
Local Features Meet Stochastic Anonymization: Revolutionizing Privacy-Preserving Face Recognition for Black-Box Models
National Engineering Research Center for Multimedia Software（国家多媒体软件工程技术研究中心）；Institute of Artificial Intelligence（人工智能研究院）；School of Computer Science（计算机学院）；Wuhan University（武汉大学）；Harbin Institute of Technology（哈尔滨工业大学）
This paper studies a novel privacy-preserving anonymization problem for pedestrian images, which preserves personal identity information (PII) for authorized models and prevents PII from being recognized by third parties. Conventional anonymization methods unavoidably cause semantic information loss, leading to limited data utility. Besides, existing learned anonymization techniques, while retaining various identity-irrelevant utilities, will change the pedestrian identity, and thus are unsuitable for training robust re-identification models. To explore the privacy-utility trade-off for pedest…
Can Video Diffusion Models Predict Past Frames? Bidirectional Cycle Consistency for Reversible Interpolation
School of Software Engineering, Xi'an Jiaotong University（西安交通大学软件工程学院）；School of Computer and Information Science, Hefei University of Technology（合肥工业大学计算机与信息科学学院）；Faculty of Science and Technology, University of Macau（澳门大学科技学院）
Concordia Institute for Information Systems Engineering（康考迪亚大学信息系统工程研究所）；Concordia University（康考迪亚大学）
Motivated by the lack of publicly available datasets of chest radiographs of positive patients with Coronavirus disease 2019 (COVID-19), we build the first-of-its-kind open dataset of synthetic COVID-19 chest X-ray images of high fidelity using an unsupervised domain adaptation approach by leveraging class conditioning and adversarial training. Our contributions are twofold. First, we show considerable performance improvements on COVID-19 detection using various deep learning architectures when employing synthetic images as additional training set. Second, we show how our image synthesis metho…
PPDM: Pixel Puzzling Diffusion Model for Speed and Memory Efficient Volumetric Medical Image Translation
Department of Radiology, Northwestern University（西北大学放射学系）；Department of Biomedical Engineering, Yale University（耶鲁大学生物医学工程系）；Department of Radiology and Biomedical Imaging, Yale School of Medicine（耶鲁医学院放射学与生物医学影像系）
Assessing the Use of Face Swapping Methods as Face Anonymizers in Videos
Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkiye（伊斯坦布尔技术大学计算机工程系）；Division of Engineering, NYU Abu Dhabi, Abu Dhabi, UAE（纽约大学阿布扎比分校工程系）
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 …
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）
School of New Media and Communication, Tianjin University（新媒体与传播学院，天津大学）；School of Artificial Intelligence, Tianjin University（人工智能学院，天津大学）；Low-Altitude Intelligence Laboratory, Xiong’an National Innovation Center（低空智能实验室，雄安国家创新中心）；Xiong’an Guochuang Lantian Technology Co., Ltd.（雄安国创蓝天科技有限公司）；National University of Defense Technology（国防科技大学）
Enhancing Privacy: The Utility of Stand-Alone Synthetic CT and MRI for Tumor and Bone Segmentation
Center Algoritmi / LASI（中心算法/拉斯伊）；Institute for AI in Medicine (IKIM)（人工智能医学研究所）；Institute of Medical Informatics（医学信息研究所）；Department of Oral and Maxillofacial Surgery（口腔和颅面外科部门）；Department of Diagnostic and Interventional Radiology（诊断和介入放射科部门）；Life and Health Sciences Research Institute (ICVS), School of Medicine（生命与健康科学研究所（ICVS），医学院）；Cancer Research Center Cologne Essen (CCCE)（科隆埃森癌症研究中心（CCCE））
Military Institute of Science and Technology (MIST)（军事科学技术学院（MIST））；Bangladesh Military Academy (BMA)（孟加拉国军事学院（BMA））
Pixel Value Ordering (PVO) holds an impressive property for high fidelity Reversible Data Hiding (RDH). In this paper, we introduce a dual-PVO (dPVO) for Prediction Error Expansion(PEE), and thereby develop a new RDH scheme to offer a better rate-distortion performance. Particularly, we propose to embed in two phases: forward and backward. In the forward phase, PVO with classic PEE is applied to every non-overlapping image block of size 1x3. In the backward phase,minimum-set and maximum-set of pixels are determined from the pixels predicted in the forward phase. The minimum set only contains t…
FlowDreamer: Exploring High Fidelity Text-to-3D Generation via Rectified Flow
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Alibaba Group（阿里巴巴集团）；The Hong Kong University of Science and Technology（香港科技大学）
StegaFFD: Privacy-Preserving Face Forgery Detection via Fine-Grained Steganographic Domain Lifting
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Future Technology, University of Chinese Academy of Sciences（中国科学院大学未来技术学院）；Beihang University（北京航空航天大学）；Macau University of Science and Technology（澳门科技大学）；Nanyang Technological University（南洋理工大学）
