Latent Ensemble Attack

Latent ensemble attacks target the underlying latent representations of data used by machine learning models, aiming to disrupt model outputs by manipulating these features rather than directly altering input data. Current research focuses on applying this approach to various models, including generative adversarial networks (GANs) and diffusion models, often employing gradient-based optimization techniques like ensemble averaging and variance reduction to improve attack effectiveness and transferability across different models. This research is significant because it reveals vulnerabilities in seemingly robust models and offers valuable insights for developing more resilient systems, particularly in applications like deepfake detection and broader adversarial machine learning security.