Defending against Model Stealing via Verifying Embedded External Features

Defending Against Model Stealing Attacks with Adaptive Misinformation

Deep Neural Networks (DNNs) are susceptible to model stealing attacks, which allows a data-limited adversary with no knowledge of the training dataset to clone the functionality of a target model, just by using black-box query access. Such attacks are typically carried out by querying the target model using inputs that are synthetically generated or sampled from a surrogate dataset to construct a labeled dataset. The adversary can use this labeled dataset to train a clone model, which achieves a classification accuracy comparable to that of the target model. We propose "Adaptive Misinformation…

Defending Against Machine Learning Model Stealing Attacks Using Deceptive Perturbations

Machine learning models are vulnerable to simple model stealing attacks if the adversary can obtain output labels for chosen inputs. To protect against these attacks, it has been proposed to limit the information provided to the adversary by omitting probability scores, significantly impacting the utility of the provided service. In this work, we illustrate how a service provider can still provide useful, albeit misleading, class probability information, while significantly limiting the success of the attack. Our defense forces the adversary to discard the class probabilities, requiring signif…

D-ADD：一种有效的模型窃取防御插件

D-ADD: An Effective Plug-In for Defending Against Model Stealing

本文提出D-ADD，一种基于账户感知分布差异的非参数检测器，结合预测中毒，作为即插即用模块有效防御模型窃取攻击，同时保持对良性用户的服务质量。

MOVE：通过嵌入外部特征实现有效且无害的模型所有权验证

MOVE: Effective and Harmless Ownership Verification via Embedded External Features

提出MOVE方法，通过风格迁移嵌入外部特征并训练元分类器，在白盒和黑盒设置下同时防御多种模型窃取，实现有效且无害的模型所有权验证。

ES Attack: Model Stealing against Deep Neural Networks without Data Hurdles

Deep neural networks (DNNs) have become the essential components for various commercialized machine learning services, such as Machine Learning as a Service (MLaaS). Recent studies show that machine learning services face severe privacy threats - well-trained DNNs owned by MLaaS providers can be stolen through public APIs, namely model stealing attacks. However, most existing works undervalued the impact of such attacks, where a successful attack has to acquire confidential training data or auxiliary data regarding the victim DNN. In this paper, we propose ES Attack, a novel model stealing att…

针对卷积神经网络模型窃取攻击的高效防御

Efficient Defense Against Model Stealing Attacks on Convolutional Neural Networks

本文提出一种简单高效的启发式输出概率扰动防御方法，无需额外训练即可抵御模型窃取攻击，在大型和量化CNN上推理延迟快37倍且对性能影响小。