  The generative autoencoders, such as the variational autoencoders or the
adversarial autoencoders, have achieved great success in lots of real-world
applications, including image generation, and signal communication.
  However, little concern has been devoted to their robustness during practical
deployment.
  Due to the probabilistic latent structure, variational autoencoders (VAEs)
may confront problems such as a mismatch between the posterior distribution of
the latent and real data manifold, or discontinuity in the posterior
distribution of the latent.
  This leaves a back door for malicious attackers to collapse VAEs from the
latent space, especially in scenarios where the encoder and decoder are used
separately, such as communication and compressed sensing.
  In this work, we provide the first study on the adversarial robustness of
generative autoencoders in the latent space.
  Specifically, we empirically demonstrate the latent vulnerability of popular
generative autoencoders through attacks in the latent space.
  We also evaluate the difference between variational autoencoders and their
deterministic variants and observe that the latter performs better in latent
robustness.
  Meanwhile, we identify a potential trade-off between the adversarial
robustness and the degree of the disentanglement of the latent codes.
  Additionally, we also verify the feasibility of improvement for the latent
robustness of VAEs through adversarial training.
  In summary, we suggest concerning the adversarial latent robustness of the
generative autoencoders, analyze several robustness-relative issues, and give
some insights into a series of key challenges.
