Reinforcement Learning-Based Black-Box Model Inversion Attacks
Gyojin Han Jaehyun Choi Haeil Lee Junmo Kim
School of Electrical Engineering, KAIST
{hangj0820, chlwogus, haeil.lee, junmo.kim}@kaist.ac.kr
Abstract
Model inversion attacks are a type of privacy attack that
reconstructs private data used to train a machine learning
model, solely by accessing the model. Recently, white-box
model inversion attacks leveraging Generative Adversarial
Networks (GANs) to distill knowledge from public datasets
have been receiving great attention because of their excel-
lent attack performance. On the other hand, current black-
box model inversion attacks that utilize GANs suffer from
issues such as being unable to guarantee the completion of
the attack process within a predetermined number of query
accesses or achieve the same level of performance as white-
box attacks. To overcome these limitations, we propose a
reinforcement learning-based black-box model inversion at-
tack. We formulate the latent space search as a Markov De-
cision Process (MDP) problem and solve it with reinforce-
ment learning. Our method utilizes the confidence scores of
the generated images to provide rewards to an agent. Fi-
nally, the private data can be reconstructed using the latent
vectors found by the agent trained in the MDP. The exper-
iment results on various datasets and models demonstrate
that our attack successfully recovers the private informa-
tion of the target model by achieving state-of-the-art attack
performance. We emphasize the importance of studies on
privacy-preserving machine learning by proposing a more
advanced black-box model inversion attack.
1. Introduction
With the rapid development of artificial intelligence,
deep learning applications are emerging in various fields
such as computer vision, healthcare, autonomous driving,
and natural language processing. As the number of cases
requiring private data to train the deep learning models in-
creases, the concern of private data leakage including sensi-
tive personal information is rising. In particular, studies on
privacy attacks [21] show that personal information can be
extracted from the trained models by malicious users. One
of the most representative privacy attacks on machine learn-
ing models is a model inversion attack, which reconstructs
the training data of a target model with only access to the
model. The model inversion attacks are divided into three
categories, 1) white-box attacks, 2) black-box attacks, and
3) label-only attacks, depending on the amount of informa-
tion of the target model. The white-box attacks can access
all parameters of the model. The black-box attacks can ac-
cess soft inference results consisting of confidence scores,
and the label-only attacks only can access inference results
in hard label forms.
The white-box model inversion attacks [5, 25, 27] have
succeeded in restoring high-quality private data including
personal information by using Generative Adversarial Net-
works (GANs) [10]. First, they train the GANs on sepa-
rate public data to learn the general prior of private data.
Then benefiting from the accessibility of the parameters of
the trained white-box models, they search and find latent
vectors that represent data of specific labels with gradient-
based optimization methods. However, these methods can-
not be applied to machine learning services such as Ama-
zon Rekognition [1] where the parameters of the model
are protected. To reconstruct private data from such ser-
vices, studies on black-box and label-only model inversion
attacks are required. Unlike the white-box attacks, these at-
tacks require methods that can explore the latent space of
the GANs in order to utilize them, as gradient-based op-
timizations are not possible. The recently proposed Model
Inversion for Deep Learning Network (MIRROR) [2] uses a
genetic algorithm to search the latent space with confidence
scores obtained from a black-box target model. In addi-
tion, Boundary-Repelling Model Inversion attack (BREP-
MI) [14] has achieved success in the label-only setting by
using a decision-based zeroth-order optimization algorithm
for latent space search.
Despite these attempts, each method has a significant is-
sue. BREP-MI starts the process of latent space search from
the first latent vector that generates an image classified as
the target class. This does not guarantee how many query
accesses will be required until the first latent vector is found
by random sampling, and in the worst case, it may not be
possible to start the search process for some target classes.
In the case of MIRROR, it performs worse than the label-
This CVPR paper is the Open Access version, provided by the Computer Vision Foundation.
Except for this watermark, it is identical to the accepted version;
the final published version of the proceedings is available on IEEE Xplore.
20504
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