Enhanced model inversion via frequency disentanglement and latent space optimization.

ABSTRACT: Model inversion attacks, which aim to reconstruct private training images from a target model's outputs, highlight the privacy risks in artificial intelligence systems. Existing methods, relying on generative adversarial networks, face challenges such as frequency-feature coupling, random latent-space sampling with initial points distant from the target identity, and insufficient loss functions for optimizing difficult samples. To address these, this paper proposes three core innovations: frequency decoupling, Top-K initialization, and dynamic focus boundary loss. Specifically, learnable filters disentangle and fuse features at multiple scales, achieving fine-grained frequency decomposition. Top-K initialization retains the best latent codes for each identity, constructing precise latent v

SUBMITTER: Yang J 

PROVIDER: S-EPMC12780102 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature