The rise of deep learning technique has raised new privacy concerns about the training data and test data. In this work, we investigate the model inversion problem in the adversarial settings, where the adversary aims at inferring information about the target model's training data and test data from the model's prediction values. We develop a solution to train a second neural network that acts as the inverse of the target model to perform the inversion. The inversion model can be trained with black-box accesses to the target model. We propose two main techniques towards training the inversion model in the adversarial settings. First, we leverage the adversary's background knowledge to compose an auxiliary set to train the inversion model, which does not require access to the original training data. Second, we design a truncation-based technique to align the inversion model to enable effective inversion of the target model from partial predictions that the adversary obtains on victim user's data. We systematically evaluate our inversion approach in various machine learning tasks and model architectures on multiple image datasets. Our experimental results show that even with no full knowledge about the target model's training data, and with only partial prediction values, our inversion approach is still able to perform accurate inversion of the target model, and outperform previous approaches.
Institute for Artificial Intelligence in Medicine（医学人工智能研究所）；Technical University of Munich（慕尼黑工业大学）；Imperial College London（帝国理工学院）
Collaborative machine learning settings like federated learning can be susceptible to adversarial interference and attacks. One class of such attacks is termed model inversion attacks, characterised by the adversary reverse-engineering the model to extract representations and thus disclose the training data. Prior implementations of this attack typically only rely on the captured data (i.e. the shared gradients) and do not exploit the data the adversary themselves control as part of the training consortium. In this work, we propose a novel model inversion framework that builds on the foundatio…
GI-PIP: Do We Require Impractical Auxiliary Dataset for Gradient Inversion Attacks?
School of Cyber Science and Technology, Beihang University（北京航空航天大学网络空间科学与技术学院）；School of Electronics and Information Engineering, Beihang University（北京航空航天大学电子信息工程学院）
本文提出GI-PIP方法，利用异常检测模型从少量辅助数据中提取图像先验分布，并以异常分数损失调控梯度反转攻击，仅用ImageNet 3.8%的数据即实现16.12 dB PSNR恢复，显著降低了对辅助数据量和分布的要求。
University of California Davis（加州大学戴维斯分校）；University of Kentucky（肯塔基大学）
To protect sensitive data in training a Generative Adversarial Network (GAN), the standard approach is to use differentially private (DP) stochastic gradient descent method in which controlled noise is added to the gradients. The quality of the output synthetic samples can be adversely affected and the training of the network may not even converge in the presence of these noises. We propose Differentially Private Model Inversion (DPMI) method where the private data is first mapped to the latent space via a public generator, followed by a lower-dimensional DP-GAN with better convergent properti…
The Chinese University of HongKong,Shenzhen（香港中文大学（深圳））
Deep learning models are vulnerable to adversarial examples, which poses an indisputable threat to their applications. However, recent studies observe gradient-masking defenses are self-deceiving methods if an attacker can realize this defense. In this paper, we propose a new defense method based on appending information. We introduce the Aux Block model to produce extra outputs as a self-ensemble algorithm and analytically investigate the robustness mechanism of Aux Block. We have empirically studied the efficiency of our method against adversarial examples in two types of white-box attacks, …
University of Science and Technology of China（中国科学技术大学）；University of Waterloo（滑铁卢大学）；CISPA Helmholtz Center for Information Security（CISPA赫尔姆霍茨信息安全中心）
Model inversion (MI) attacks have raised increasing concerns about privacy, which can reconstruct training data from public models. Indeed, MI attacks can be formalized as an optimization problem that seeks private data in a certain space. Recent MI attacks leverage a generative adversarial network (GAN) as an image prior to narrow the search space, and can successfully reconstruct even the high-dimensional data (e.g., face images). However, these generative MI attacks do not fully exploit the potential capabilities of the target model, still leading to a vague and coupled search space, i.e., …
University of Toronto（多伦多大学）；Vector Institute（向量研究所）；Simon Fraser University（西蒙弗雷泽大学）
Given the ubiquity of deep neural networks, it is important that these models do not reveal information about sensitive data that they have been trained on. In model inversion attacks, a malicious user attempts to recover the private dataset used to train a supervised neural network. A successful model inversion attack should generate realistic and diverse samples that accurately describe each of the classes in the private dataset. In this work, we provide a probabilistic interpretation of model inversion attacks, and formulate a variational objective that accounts for both diversity and accur…
The performance of deep learning models highly depends on the amount of training data. It is common practice for today's data holders to merge their datasets and train models collaboratively, which yet poses a threat to data privacy. Different from existing methods such as secure multi-party computation (MPC) and federated learning (FL), we find representation learning has unique advantages in collaborative learning due to the lower communication overhead and task-independency. However, data representations face the threat of model inversion attacks. In this article, we formally define the col…
Rethinking Model Inversion Attacks With Patch-Wise Reconstruction
Institute of New Media and Communications, Seoul National University（首尔大学新媒体与通信研究所）；Pohang University of Science and Technology (POSTECH)（浦项科技大学）；Department of Electrical and Computer Engineering, Seoul National University（首尔大学电气与计算机工程系）
Generative adversarial networks (GAN) are a class of powerful machine learning techniques, where both a generative and discriminative model are trained simultaneously. GANs have been used, for example, to successfully generate "deep fake" images. A recent trend in malware research consists of treating executables as images and employing image-based analysis techniques. In this research, we generate fake malware images using auxiliary classifier GANs (AC-GAN), and we consider the effectiveness of various techniques for classifying the resulting images. Our results indicate that the resulting mu…
University of California, Merced（加州大学默塞德分校）；NEC Laboratories America（NEC美国实验室）；University of California, San Diego（加州大学圣地亚哥分校）
Data privacy has emerged as an important issue as data-driven deep learning has been an essential component of modern machine learning systems. For instance, there could be a potential privacy risk of machine learning systems via the model inversion attack, whose goal is to reconstruct the input data from the latent representation of deep networks. Our work aims at learning a privacy-preserving and task-oriented representation to defend against such model inversion attacks. Specifically, we propose an adversarial reconstruction learning framework that prevents the latent representations decode…
When and Where do Data Poisons Attack Textual Inversion?
College of Computing and Data Science, Nanyang Technological University, Singapore（计算与数据科学学院，南洋理工大学，新加坡）；Rapid-Rich Object Search (ROSE) Lab, Nanyang Technological University, Singapore（快速丰富对象搜索（ROSE）实验室，南洋理工大学，新加坡）
Adversarial examples are typically constructed by perturbing an existing data point within a small matrix norm, and current defense methods are focused on guarding against this type of attack. In this paper, we propose unrestricted adversarial examples, a new threat model where the attackers are not restricted to small norm-bounded perturbations. Different from perturbation-based attacks, we propose to synthesize unrestricted adversarial examples entirely from scratch using conditional generative models. Specifically, we first train an Auxiliary Classifier Generative Adversarial Network (AC-GA…
Tech University of Korea（韩国科技大学）；University of Sindh（信德大学）；Munster Technological University（明斯特理工大学）；University of Johannesburg（约翰内斯堡大学）；Sheffield Hallam University（谢菲尔德哈勒姆大学）；Sungkyunkwan University（成均馆大学）
The past decade has seen a rapid adoption of Artificial Intelligence (AI), specifically the deep learning networks, in Internet of Medical Things (IoMT) ecosystem. However, it has been shown recently that the deep learning networks can be exploited by adversarial attacks that not only make IoMT vulnerable to the data theft but also to the manipulation of medical diagnosis. The existing studies consider adding noise to the raw IoMT data or model parameters which not only reduces the overall performance concerning medical inferences but also is ineffective to the likes of deep leakage from gradi…
