  Deep compressed sensing assumes the data has sparse representation in a
latent space, i.e., it is intrinsically of low-dimension. The original data is
assumed to be mapped from a low-dimensional space through a
low-to-high-dimensional generator. In this work, we propound how to design such
a low-to-high dimensional deep learning-based generator suiting for compressed
sensing, while satisfying robustness to universal adversarial perturbations in
the latent domain. We also justify why the noise is considered in the latent
space. The work is also buttressed with theoretical analysis on the robustness
of the trained generator to adversarial perturbations. Experiments on
real-world datasets are provided to substantiate the efficacy of the proposed
\emph{generative model adversarial training for deep compressed sensing.}
