MIBench: A Comprehensive Benchmark for Model Inversion Attack and Defense
<|reference_start|>MIBench: A Comprehensive Benchmark for Model Inversion Attack and Defense: Model Inversion (MI) attacks aim at leveraging the output information of target models to reconstruct privacy-sensitive training data, raising widespread concerns on privacy threats of Deep Neural Networks (DNNs). Unfortunately, in tandem with the rapid evolution of MI attacks, the lack of a comprehensive, aligned, and reliable benchmark has emerged as a formidable challenge. This deficiency leads to inadequate comparisons between different attack methods and inconsistent experimental setups. In this paper, we introduce the first practical benchmark for model inversion attacks and defenses to address this critical gap, which is named \textit{MIBench}. This benchmark serves as an extensible and reproducible modular-based toolbox and currently integrates a total of 16 state-of-the-art attack and defense methods. Moreover, we furnish a suite of assessment tools encompassing 9 commonly used evaluation protocols to facilitate standardized and fair evaluation and analysis. Capitalizing on this foundation, we conduct extensive experiments from multiple perspectives to holistically compare and analyze the performance of various methods across different scenarios, which overcomes the misalignment issues and discrepancy prevalent in previous works. Based on the collected attack methods and defense strategies, we analyze the impact of target resolution, defense robustness, model predictive power, model architectures, transferability and loss function. Our hope is that this \textit{MIBench} could provide a unified, practical and extensible toolbox and is widely utilized by researchers in the field to rigorously test and compare their novel methods, ensuring equitable evaluations and thereby propelling further advancements in the future development.<|reference_end|>
@article{qiu2024mibench:,
    title={MIBench: A Comprehensive Benchmark for Model Inversion Attack and
  Defense},
    author={Yixiang Qiu, Hongyao Yu, Hao Fang, Wenbo Yu, Bin Chen, Xuan Wang,
  Shu-Tao Xia, Ke Xu},
    journal={arXiv preprint arXiv:2410.05159},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05159},
    primaryClass={cs.CV cs.CR}
}