Model Extraction and Defenses on Generative Adversarial Networks
- URL: http://arxiv.org/abs/2101.02069v1
- Date: Wed, 6 Jan 2021 14:36:21 GMT
- Title: Model Extraction and Defenses on Generative Adversarial Networks
- Authors: Hailong Hu, Jun Pang
- Abstract summary: We study the feasibility of model extraction attacks against generative adversarial networks (GANs)
We propose effective defense techniques to safeguard GANs, considering a trade-off between the utility and security of GAN models.
- Score: 0.9442139459221782
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Model extraction attacks aim to duplicate a machine learning model through
query access to a target model. Early studies mainly focus on discriminative
models. Despite the success, model extraction attacks against generative models
are less well explored. In this paper, we systematically study the feasibility
of model extraction attacks against generative adversarial networks (GANs).
Specifically, we first define accuracy and fidelity on model extraction attacks
against GANs. Then we study model extraction attacks against GANs from the
perspective of accuracy extraction and fidelity extraction, according to the
adversary's goals and background knowledge. We further conduct a case study
where an adversary can transfer knowledge of the extracted model which steals a
state-of-the-art GAN trained with more than 3 million images to new domains to
broaden the scope of applications of model extraction attacks. Finally, we
propose effective defense techniques to safeguard GANs, considering a trade-off
between the utility and security of GAN models.
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