As more and more personal photos are shared and tagged in social media, avoiding privacy risks such as unintended recognition becomes increasingly challenging. We propose a new hybrid approach to obfuscate identities in photos by head replacement. Our approach combines state of the art parametric face synthesis with latest advances in Generative Adversarial Networks (GAN) for data-driven image synthesis. On the one hand, the parametric part of our method gives us control over the facial parameters and allows for explicit manipulation of the identity. On the other hand, the data-driven aspects allow for adding fine details and overall realism as well as seamless blending into the scene context. In our experiments, we show highly realistic output of our system that improves over the previous state of the art in obfuscation rate while preserving a higher similarity to the original image content.
We present a novel variational generative adversarial network (VGAN) based on Wasserstein loss to learn a latent representation from a face image that is invariant to identity but preserves head-pose information. This facilitates synthesis of a realistic face image with the same head pose as a given input image, but with a different identity. One application of this network is in privacy-sensitive scenarios; after identity replacement in an image, utility, such as head pose, can still be recovered. Extensive experimental validation on synthetic and real human-face image datasets performed unde…
PAI-Studio: Cinematic Video Background Replacement with Camera-Aware Motion
Utopai Studios（Utopai工作室）；Nanyang Technological University（南洋理工大学）；University of California, Irvine（加州大学尔湾分校）；Show Lab, National University of Singapore（新加坡国立大学Show实验室）
PIU: Proximity-guided Identity Unlearning in ID-Conditioned Diffusion Models
University of Ljubljana, Faculty of Computer and Information Science（卢布尔雅那大学计算机与信息科学系）；University of Ljubljana, Faculty of Electrical Engineering（卢布尔雅那大学电子工程系）
Queen Mary University of London（伦敦玛丽女王大学）；University of Trento（特伦托大学）
This work addresses the problem of anonymizing the identity of faces in a dataset of images, such that the privacy of those depicted is not violated, while at the same time the dataset is useful for downstream task such as for training machine learning models. To the best of our knowledge, we are the first to explicitly address this issue and deal with two major drawbacks of the existing state-of-the-art approaches, namely that they (i) require the costly training of additional, purpose-trained neural networks, and/or (ii) fail to retain the facial attributes of the original images in the anon…
DecoyFace: Beyond Obfuscation via Controllable and Imperceptible Identity Misdirection for Privacy-Preserving Face Recognition
School of Information and Communications Engineering, Xi’an Jiaotong University（西安交通大学信息与通信工程学院）；Shaanxi Key Laboratory of Deep Space Exploration Intelligent Information Technology（陕西省深空探测智能信息技术重点实验室）
GaitProtector: Impersonation-Driven Gait De-Identification via Training-Free Diffusion Latent Optimization
City University of New York（纽约城市大学）；Wuhan University（武汉大学）；University of Aberdeen（阿伯丁大学）；Nanyang Technological University（南洋理工大学）；ELLIS Institute Finland（芬兰ELLIS研究所）；University of Oulu（奥卢大学）
An Empirical Study of Validating Synthetic Data for Text-Based Person Retrieval
School of Computer Science and Technology, Soochow University（苏州大学计算机科学与技术学院）；Frontis；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所基础模型研究中心）；School of Artifcial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Wuhan AI Research, Wuhan, China（武汉人工智能研究所，武汉，中国）；School of Computer Science, Wuhan University（武汉大学计算机学院）
As billions of personal data being shared through social media and network, the data privacy and security have drawn an increasing attention. Several attempts have been made to alleviate the leakage of identity information from face photos, with the aid of, e.g., image obfuscation techniques. However, most of the present results are either perceptually unsatisfactory or ineffective against face recognition systems. Our goal in this paper is to develop a technique that can encrypt the personal photos such that they can protect users from unauthorized face recognition systems but remain visually…
BlobCtrl: Taming Controllable Blob for Element-level Image Editing
SECE, Peking University（北京大学SECE学院）；The Chinese University of Hong Kong（香港中文大学）；ARC Lab, Tencent（腾讯ARC实验室）；The Hong Kong University of Science and Technology（香港科学与技术大学）；GVC Lab, Great Bay University（Great Bay大学GVC实验室）
PixelFade: Privacy-preserving Person Re-identification with Noise-guided Progressive Replacement
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education（教育部机器智能与先进计算重点实验室）；Guangdong Province Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室）
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）
Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Google（谷歌）；Johns Hopkins University（约翰斯·霍普金斯大学）
Graph representation of objects and their relations in a scene, known as a scene graph, provides a precise and discernible interface to manipulate a scene by modifying the nodes or the edges in the graph. Although existing works have shown promising results in modifying the placement and pose of objects, scene manipulation often leads to losing some visual characteristics like the appearance or identity of objects. In this work, we propose DisPositioNet, a model that learns a disentangled representation for each object for the task of image manipulation using scene graphs in a self-supervised …
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）
