MIBench: A Comprehensive Framework for Benchmarking Model Inversion Attack and Defense
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913726456659968 |
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| author | Qiu, Yixiang Yu, Hongyao Fang, Hao Zhuang, Tianqu Yu, Wenbo Chen, Bin Wang, Xuan Xia, Shu-Tao Xu, Ke |
| author_facet | Qiu, Yixiang Yu, Hongyao Fang, Hao Zhuang, Tianqu Yu, Wenbo Chen, Bin Wang, Xuan Xia, Shu-Tao Xu, Ke |
| contents | Model Inversion (MI) attacks aim at leveraging the output information of target models to reconstruct privacy-sensitive training data, raising critical concerns regarding the privacy vulnerabilities of Deep Neural Networks (DNNs). Unfortunately, in tandem with the rapid evolution of MI attacks, the absence of a comprehensive benchmark with standardized metrics and reproducible implementations has emerged as a formidable challenge. This deficiency has hindered objective comparison of methodological advancements and reliable assessment of defense efficacy. To address this critical gap, we build the first practical benchmark named MIBench for systematic evaluation of model inversion attacks and defenses. This benchmark bases on an extensible and reproducible modular-based toolbox which currently integrates a total of 19 state-of-the-art attack and defense methods and encompasses 9 standardized evaluation protocols. Capitalizing on this foundation, we conduct extensive evaluation from multiple perspectives to holistically compare and analyze various methods across different scenarios, such as the impact of target resolution, model predictive power, defense performance and adversarial robustness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05159 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MIBench: A Comprehensive Framework for Benchmarking Model Inversion Attack and Defense Qiu, Yixiang Yu, Hongyao Fang, Hao Zhuang, Tianqu Yu, Wenbo Chen, Bin Wang, Xuan Xia, Shu-Tao Xu, Ke Computer Vision and Pattern Recognition Cryptography and Security Model Inversion (MI) attacks aim at leveraging the output information of target models to reconstruct privacy-sensitive training data, raising critical concerns regarding the privacy vulnerabilities of Deep Neural Networks (DNNs). Unfortunately, in tandem with the rapid evolution of MI attacks, the absence of a comprehensive benchmark with standardized metrics and reproducible implementations has emerged as a formidable challenge. This deficiency has hindered objective comparison of methodological advancements and reliable assessment of defense efficacy. To address this critical gap, we build the first practical benchmark named MIBench for systematic evaluation of model inversion attacks and defenses. This benchmark bases on an extensible and reproducible modular-based toolbox which currently integrates a total of 19 state-of-the-art attack and defense methods and encompasses 9 standardized evaluation protocols. Capitalizing on this foundation, we conduct extensive evaluation from multiple perspectives to holistically compare and analyze various methods across different scenarios, such as the impact of target resolution, model predictive power, defense performance and adversarial robustness. |
| title | MIBench: A Comprehensive Framework for Benchmarking Model Inversion Attack and Defense |
| topic | Computer Vision and Pattern Recognition Cryptography and Security |
| url | https://arxiv.org/abs/2410.05159 |