Deep neural networks are extensively applied to real-world tasks, such as face recognition and medical image classification, where privacy and data protection are critical. Image data, if not protected, can be exploited to infer personal or contextual information. Existing privacy preservation methods, like encryption, generate perturbed images that are unrecognizable to even humans. Adversarial attack approaches prohibit automated inference even for authorized stakeholders, limiting practical incentives for commercial and widespread adaptation. This pioneering study tackles an unexplored practical privacy preservation use case by generating human-perceivable images that maintain accurate inference by an authorized model while evading other unauthorized black-box models of similar or dissimilar objectives, and addresses the previous research gaps. The datasets employed are ImageNet, for image classification, Celeba-HQ dataset, for identity classification, and AffectNet, for emotion classification. Our results show that the generated images can successfully maintain the accuracy of a protected model and degrade the average accuracy of the unauthorized black-box models to 11.97%, 6.63%, and 55.51% on ImageNet, Celeba-HQ, and AffectNet datasets, respectively.
University of Science and Technology of China（中国科学技术大学）；University of Tsukuba（筑波大学）；RIKEN AIP（理化学研究所革新智能综合研究中心）
In this study, we propose a new methodology to control how user's data is recognized and used by AI via exploiting the properties of adversarial examples. For this purpose, we propose reversible adversarial example (RAE), a new type of adversarial example. A remarkable feature of RAE is that the image can be correctly recognized and used by the AI model specified by the user because the authorized AI can recover the original image from the RAE exactly by eliminating adversarial perturbation. On the other hand, other unauthorized AI models cannot recognize it correctly because it functions as a…
To See is Not to Learn: Protecting Multimodal Data from Unauthorized Fine-Tuning of Large Vision-Language Model
School of Computing；Augmented Intelligence, Arizona State University, Tempe, AZ, USA；Department of Computer Science；Engineering, Texas A\&M University, College Station, TX, USA
DP-TRAE: A Dual-Phase Merging Transferable Reversible Adversarial Example for Image Privacy Protection
School of Computer and Information Engineering, Xiamen University of Technology（厦门理工大学计算机与信息工程学院）；School of Computer Science, Engineering Research Center of Machine Learning and Industry Intelligence, Sichuan University（四川大学计算机科学学院，机器学习与产业智能工程研究中心）；Department of Computer and Information Science, Faculty of Science and Technology, University of Macau（澳门大学科学与技术学院计算机与信息科学系）；Department of Electrical and Electronic Engineering, University of Hong Kong（香港大学电子与电气工程系）；Institute of Space Internet, Fudan University（复旦大学空间互联网研究所）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）
Diffusion-based Adversarial Identity Manipulation for Facial Privacy Protection
School of Computer Science；Engineering, Sun Yat-sen University Guangzhou China；State Key Laboratory of Mathematical Engineering；Engineering, Sun Yat-sen University
A Gray-box Attack against Latent Diffusion Model-based Image Editing by Posterior Collapse
School of Computer Science, University of St Andrews（圣安德鲁大学计算机科学学院）；Department of Data Science, City University of Hong Kong（香港城市大学数据科学系）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Computer Science, University of Nottingham（诺丁汉大学计算机科学学院）；Department of Data Science and Artificial Intelligence, Monash University（墨尔本大学数据科学与人工智能系）；College of Information Science & Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）
Transferable Adversarial Facial Images for Privacy Protection
School of Software Engineering, Huazhong University of Science and Technology（华中科技大学软件学院）；School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；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）
Improving Adversarial Transferability by Stable Diffusion
National University of Singapore（新加坡国立大学）；Shanghai Jiao Tong University（上海交通大学）；Nanyang Technological University（南洋理工大学）；University of Science and Technology of China（中国科学技术大学）
University of Malaya（马来亚大学）；WeBank（微众银行）；Hong Kong University of Science and Technology（香港科技大学）
Ever since Machine Learning as a Service (MLaaS) emerges as a viable business that utilizes deep learning models to generate lucrative revenue, Intellectual Property Right (IPR) has become a major concern because these deep learning models can easily be replicated, shared, and re-distributed by any unauthorized third parties. To the best of our knowledge, one of the prominent deep learning models - Generative Adversarial Networks (GANs) which has been widely used to create photorealistic image are totally unprotected despite the existence of pioneering IPR protection methodology for Convolutio…
Huazhong University of Science and Technology（华中科技大学）；National Engineering Research Center for Big Data Technology and System（国家大数据技术与系统工程研究中心）；Hubei Engineering Research Center on Big Data Security（湖北省大数据安全工程研究中心）；Deakin University（迪肯大学）
While deep face recognition (FR) systems have shown amazing performance in identification and verification, they also arouse privacy concerns for their excessive surveillance on users, especially for public face images widely spread on social networks. Recently, some studies adopt adversarial examples to protect photos from being identified by unauthorized face recognition systems. However, existing methods of generating adversarial face images suffer from many limitations, such as awkward visual, white-box setting, weak transferability, making them difficult to be applied to protect face priv…
