Enhanced model inversion via frequency disentanglement and latent space optimization.

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 vectors. The dynamic focus boundary loss, inspired by focal loss, prevents overfitting to easy samples and focuses on difficult ones. Experiments on CelebA, FFHQ, and FaceScrub datasets demonstrate that our method significantly enhances attack performance, especially under large data-distribution shifts.