a fine-grained hybrid inversion-based membership inference attack against gans

Generative adversarial networks (gans) are commonly used in various applications. different membership inference attacks have been carried out against gans. however, the accuracy of these attacks decreases with a large number of training samples, and there have been no attacks conducted against privacy-preserving gan models with dependent or independent datasets. therefore, this paper proposes a fine-grained inversion-based attack. in this proposed attack, fine-grained reconstruction error is utilized to infer the membership or non-membership of given samples. to calculate thereconstruction error, an inversion-based encoder is trained, and the latent code obtained from the encoder is refined using a genetic algorithm. the membership status of the candidate target sample is determined using the reconstruction error of the segmentations of the target sample. the proposed attack can be executed by accessing the generator network in both black and white-box settings. the accuracy of the proposed attack is compared with other relevant studies, demonstrating its superior performance. furthermore, the results indicate that privacy-preserving mechanisms do not ensure that dependent data does not disclose information about individual samples.