PPO-MI: Efficient Black-Box Model Inversion via Proximal Policy Optimization
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917930792386560 |
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| author | Shou, Xinpeng |
| author_facet | Shou, Xinpeng |
| contents | Model inversion attacks pose a significant privacy risk by attempting to reconstruct private training data from trained models. Most of the existing methods either depend on gradient estimation or require white-box access to model parameters, which limits their applicability in practical scenarios. In this paper, we propose PPO-MI, a novel reinforcement learning-based framework for black-box model inversion attacks. Our approach formulates the inversion task as a Markov Decision Process, where an agent navigates the latent space of a generative model to reconstruct private training samples using only model predictions. By employing Proximal Policy Optimization (PPO) with a momentum-based state transition mechanism, along with a reward function balancing prediction accuracy and exploration, PPO-MI ensures efficient latent space exploration and high query efficiency. We conduct extensive experiments illustrates that PPO-MI outperforms the existing methods while require less attack knowledge, and it is robust across various model architectures and datasets. These results underline its effectiveness and generalizability in practical black-box scenarios, raising important considerations for the privacy vulnerabilities of deployed machine learning models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14370 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PPO-MI: Efficient Black-Box Model Inversion via Proximal Policy Optimization Shou, Xinpeng Machine Learning Computer Vision and Pattern Recognition Model inversion attacks pose a significant privacy risk by attempting to reconstruct private training data from trained models. Most of the existing methods either depend on gradient estimation or require white-box access to model parameters, which limits their applicability in practical scenarios. In this paper, we propose PPO-MI, a novel reinforcement learning-based framework for black-box model inversion attacks. Our approach formulates the inversion task as a Markov Decision Process, where an agent navigates the latent space of a generative model to reconstruct private training samples using only model predictions. By employing Proximal Policy Optimization (PPO) with a momentum-based state transition mechanism, along with a reward function balancing prediction accuracy and exploration, PPO-MI ensures efficient latent space exploration and high query efficiency. We conduct extensive experiments illustrates that PPO-MI outperforms the existing methods while require less attack knowledge, and it is robust across various model architectures and datasets. These results underline its effectiveness and generalizability in practical black-box scenarios, raising important considerations for the privacy vulnerabilities of deployed machine learning models. |
| title | PPO-MI: Efficient Black-Box Model Inversion via Proximal Policy Optimization |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.14370 |