  Despite advancements in Autoencoders (AEs) for tasks like dimensionality
reduction, representation learning and data generation, they remain vulnerable
to adversarial attacks. Variational Autoencoders (VAEs), with their
probabilistic approach to disentangling latent spaces, show stronger resistance
to such perturbations compared to deterministic AEs; however, their resilience
against adversarial inputs is still a concern. This study evaluates the
robustness of VAEs against non-targeted adversarial attacks by optimizing
minimal sample-specific perturbations to cause maximal damage across diverse
demographic subgroups (combinations of age and gender). We investigate two
questions: whether there are robustness disparities among subgroups, and what
factors contribute to these disparities, such as data scarcity and
representation entanglement. Our findings reveal that robustness disparities
exist but are not always correlated with the size of the subgroup. By using
downstream gender and age classifiers and examining latent embeddings, we
highlight the vulnerability of subgroups like older women, who are prone to
misclassification due to adversarial perturbations pushing their
representations toward those of other subgroups.
