3 accepted papers
Model inversion attacks (MIAs) aim to reconstruct class-representative samples from trained models. Recent generative MIAs utilize generative adversarial networks to learn image priors that guide the inversion process, yielding reconstructions with high visual quality and strong fidelity to the priv…
In model inversion attacks (MIAs), adversaries attempt to recover private training data by exploiting access to a well-trained target model. Recent advancements have improved MIA performance using a two-stage generative framework. This approach first employs a generative adversarial network to learn…
With the rapid development of vision-based deep learning (DL), it is an effective method to generate large-scale synthetic data to supplement real data to train the DL models for domain adaptation. However, previous vanilla domain adaptation methods generally assume the same label space, and such an…