Label-Only Model Inversion Attacks via Knowledge Transfer

• URL: http://arxiv.org/abs/2310.19342v1
• Date: Mon, 30 Oct 2023 08:32:12 GMT
• Title: Label-Only Model Inversion Attacks via Knowledge Transfer
• Authors: Ngoc-Bao Nguyen, Keshigeyan Chandrasegaran, Milad Abdollahzadeh, Ngai-Man Cheung
• Abstract summary: In a model inversion (MI) attack, an adversary abuses access to a machine learning (ML) model to infer and reconstruct private data. We propose LOKT, a novel approach for label-only MI attacks. Our method significantly outperforms existing SOTA Label-only MI attack by more than 15% across all MI benchmarks.
• Score: 35.42380723970432
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In a model inversion (MI) attack, an adversary abuses access to a machine learning (ML) model to infer and reconstruct private training data. Remarkable progress has been made in the white-box and black-box setups, where the adversary has access to the complete model or the model's soft output respectively. However, there is very limited study in the most challenging but practically important setup: Label-only MI attacks, where the adversary only has access to the model's predicted label (hard label) without confidence scores nor any other model information. In this work, we propose LOKT, a novel approach for label-only MI attacks. Our idea is based on transfer of knowledge from the opaque target model to surrogate models. Subsequently, using these surrogate models, our approach can harness advanced white-box attacks. We propose knowledge transfer based on generative modelling, and introduce a new model, Target model-assisted ACGAN (T-ACGAN), for effective knowledge transfer. Our method casts the challenging label-only MI into the more tractable white-box setup. We provide analysis to support that surrogate models based on our approach serve as effective proxies for the target model for MI. Our experiments show that our method significantly outperforms existing SOTA Label-only MI attack by more than 15% across all MI benchmarks. Furthermore, our method compares favorably in terms of query budget. Our study highlights rising privacy threats for ML models even when minimal information (i.e., hard labels) is exposed. Our study highlights rising privacy threats for ML models even when minimal information (i.e., hard labels) is exposed. Our code, demo, models and reconstructed data are available at our project page: https://ngoc-nguyen-0.github.io/lokt/

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